Next Article in Journal
Influence of the Objective Function in the Dynamic Model Updating of Girder Bridge Structures
Previous Article in Journal
Reinforcing the Flexural Fracture Zone in the Xiangjiaba Hydropower Station by Simultaneously Applying Wet-Milling Cement and Chemical Compound Grouting
Previous Article in Special Issue
Do Low-Income Housing Tax Credit Developments Expand Neighborhood Opportunities?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 15
Issue 3
10.3390/buildings15030342
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Enjoying Your Neighbourhood During the COVID-19 Pandemic? A Hong Kong Study on Housing-Related Anti-Social Behaviour

by
Yung Yau
School of Graduate Studies and Department of Sociology and Social Policy, Lingnan University, Hong Kong 999077, China
Buildings 2025, 15(3), 342; https://doi.org/10.3390/buildings15030342
Submission received: 28 November 2024 / Revised: 18 January 2025 / Accepted: 22 January 2025 / Published: 23 January 2025
(This article belongs to the Special Issue Real Estate, Housing and Urban Governance)
Versions Notes

Abstract

:
Studies have established a negative correlation between residents’ perceptions of anti-social behaviours (ASBs) and their sense of community (SOC) within their neighbourhoods. Yet, whether their relationship persisted during the COVID-19 pandemic was under-researched, when daily life significantly changed. Theoretically, the pandemic’s impact on ASB and SOC was multifaceted, as lockdowns and work-from-home arrangements led to an increased time spent at home, potentially exacerbating neighbour nuisances and vulnerability to ASB, but also fostered opportunities for community building. Residents spent more time in their neighbourhoods engaging in neighbourly interactions and mutual aid initiatives, potentially strengthening SOC. To investigate these contrasting effects, this study analysed data from two questionnaire surveys conducted among private housing residents in Hong Kong before and after the onset of the pandemic. It finds that perceived ASB seriousness and SOC levels increased during the pandemic, yet the negative correlation between ASBs and SOC persisted. This research contributes to the literature by exploring the pandemic’s effects on SOC and ASB in high-density, high-rise contexts and expanding beyond noise nuisances to include various unacceptable behaviours in assessing ASBs. The paper concludes with policy implications and outlines a future research agenda focusing on the interplay between ASB control and SOC development in pandemic scenarios.

1. Introduction

Neighbour conflicts and nuisances are becoming increasingly prevalent in cities characterised by high-rise, high-density housing developments. In such a housing setting, the residents’ coexistence and territorial and spatial proximity may not contribute to the strength of social ties between neighbours [1]. Indeed, this setting generates more nuisance complaints and anti-social behaviours (ASBs) among neighbours. It has been extensively documented that communities with stronger social ties or cohesion tend to have fewer neighbourhood problems, including crimes and ASBs [2,3,4]. Therefore, scholars have highlighted the prospect of a communitarian approach to tackle ASB problems in residential neighbourhoods [4,5,6].
Nonetheless, the COVID-19 pandemic, which emerged in late 2019, significantly transformed urban life worldwide through enforced lockdowns and social distancing measures. During these restrictions, homes became central to daily activities, shifting how residents perceived their living environments [7]. The pandemic also affected neighbourhood dynamics, fostering increased interactions among residents. Studies indicated that, despite mobility restrictions, social connections among neighbours improved, resulting in a stronger sense of community (SOC) and mutual support [8,9]. This sense of solidarity was further enhanced as residents engaged in mutual aid, helping one another with resources and care during the crisis. Consequently, many reported better relationships with their neighbours, although experiences varied based on individual circumstances and prior community ties. On the other hand, the pandemic also increased conflicts among neighbours due to prolonged cohabitation in close quarters, leading to tensions arising from differing lifestyles and noise disturbances [10,11]. Overall, the pandemic has reshaped urban living, highlighting both the potential for enhanced community bonds and the challenges of increased tensions in shared living spaces.
From the above, it is clear that the COVID-19 pandemic could influence housing-related ASBs and SOC. Although the negative relationship between residents’ perceptions of ASBs and SOC within their residential neighbourhoods has been proven in the pre-pandemic era, whether this relationship was altered during the COVID-19 pandemic has not been investigated. Against this background, this study explores whether the COVID-19 pandemic changed the landscape of housing-related ASB in Hong Kong’s high-rise residential neighbourhoods. It also examines how the pandemic affected the proven association between the perceived seriousness of ASBs and SOC. These goals are achieved through analyses of data collected from two structured questionnaire surveys administered to private housing residents in Hong Kong. For this study, only traditional housing-related ASBs are investigated; ASBs related to non-conformance with the pandemic control measures are disregarded.
The study makes three contributions to the literature. First, there has been little research on the effects of the pandemic on SOC and housing-related ASB in a high-rise, high-density setting. This empirical study of high-rise private housing communities in Hong Kong can fill this research gap. Second, the literature on the impacts of the pandemic on ASBs is focused on noise nuisances. Still, a wide array of unacceptable behaviours can be categorised as housing-related ASBs. Accordingly, this study takes a more multidimensional approach to ASBs. Third, this study attempts to answer whether a communitarian approach still plays a role in ASB management during pandemics.

2. Literature Review

2.1. COVID-19 Pandemic and ASBs

The COVID-19 pandemic, which broke out in late 2019, and the associated non-pharmacological measures (such as lockdowns and social distancing) to control the spread of the disease have altered the daily lives of many urban residents worldwide. During lockdown periods, the home environment became the dominant place for daily activities [12]. Apart from mandatory ‘stay-at-home’ orders, the ‘work-from-home’ practices voluntarily adopted by many private-sector and public-sector organisations also led people to spend more time at home during the pandemic. The pandemic reshaped how people perceive their living environment and neighbourhoods [12,13]. For example, residents became less satisfied with the space available in their apartments after prolonged exposure to their home environment during lockdowns [13].
Outside of family life, the pandemic also changed people’s neighbourhood lives and interactions with their neighbours. On the positive side, many empirical studies have shown that spending more time in their neighbourhoods gave residents more opportunities to interact with their neighbours. For example, Erin and Çubukçu found that despite the mobility restrictions and social distancing controls, social interactions among neighbours generally increased, and residents tended to know their neighbours more (or better) during the pandemic than they did beforehand [14]. More social interactions with neighbours led to a stronger SOC and higher mutual support [15,16].
Moreover, the outbreak of the COVID-19 pandemic can be seen as a crisis or emergency that necessitated mutual support and assistance within local communities. In theory, such a scenario could enhance solidarity, cooperation, and social trust within communities [17,18,19], thus contributing to community building. During local COVID-19 outbreaks, mutual aid among neighbours was practised through caring for the sick and meeting people’s needs by sharing resources (e.g., surgical masks, food, and other daily necessities). The pandemic also motivated residents to participate more actively in neighbourhood affairs. Therefore, it is not surprising to see reports that people generally experienced an improvement in their relations with neighbours during the pandemic. However, such improvement might vary across individuals with different characteristics, such as levels of resources and prior embeddedness in their neighbourhoods [20,21,22].
On the negative side, a high degree of conflicts between neighbours was an expected result of the pandemic. It led to people with different rights and interests and religious, customary, cultural, and social backgrounds being physically and spatially restricted in their living places. More time spent at home may exacerbate conflicts between neighbours [23]. A more considerable overlap of stay-at-home periods of people with different living styles left some residents more vulnerable to nuisances from their neighbours. Furthermore, spending more time at home might mean that people were engaged more regularly in nuisance-generating (particularly noise-generating) activities, such as doing home repairs, exercising, listening to loud music, watching television, and children playing [24].
Nonetheless, empirical studies have returned mixed results on the impacts of the COVID-19 pandemic on neighbour disputes or conflicts. For instance, it was generally reported that residents reported more noise nuisances or annoyances after the pandemic outbreak compared with the pre-pandemic period [11,13,25]. It was also discovered that noise complaints were significantly more frequent across London during lockdowns [26]. Conversely, a study in Italy found that lockdowns resulted in a general reduction in noise problems [27], and similar findings were reported by other researchers [24,28] who compared the number of noise complaints reported across different cities in the United States before and during the pandemic. Another opinion-based survey conducted in Turkey discovered that noise annoyance attributed to neighbours did not change significantly before and during the pandemic [29]. Neighbour disputes were also found to decrease during the pandemic [13].

2.2. Resilience, SOC, and ASBs

The COVID-19 pandemic may have illuminated the intricate relationships between resilience, ASBs, and SOC. Originating in ecology in the 1970s, resilience refers to the ability to return to a normal state after a disruption [30]. It concerns the capacity of individuals and communities to adapt positively to challenges and stressors. Resilience is also regarded as a dynamic process encompassing the ability to recover from adversity while maintaining or enhancing well-being [31]. Resilient communities are characterised by strong social networks, effective communication, and the ability to mobilise resources in times of need [32,33].
Research has shown that resilience is not solely an individual trait but a collective attribute that can be developed through community engagement and social cohesion [34,35]. For instance, communities that foster strong relationships among residents often demonstrate greater resilience when faced with challenges, such as economic downturns or natural disasters [36]. This collective resilience is vital in addressing conflicts arising from neighbour nuisances, as it encourages collaborative problem-solving and mutual support. It has been evidenced that fostering a sense of community can enhance resilience [37]. Communities prioritising social cohesion and collective identity are more likely to address nuisances and other challenges effectively. This cyclical relationship suggests that initiatives to enhance community engagement and resilience can effectively mitigate the effects of ASBs.

3. Materials and Methods

The analysis of extensive datasets obtained from the government has become a prevailing trend in research on contemporary urban phenomena and patterns. These datasets, which consist of secondary data, offer naturally occurring records of what happens within local neighbourhoods. However, these records can only be taken advantage of in places with open-access municipal government data. For example, in the United States and Canada, complaints against neighbourhood nuisances can be extracted from the dataset of the 311 non-emergency calls for government services and information. Similar datasets are available from some city councils in Australia. For instance, to interrogate neighbour complaints in Brisbane, Australia, Liu et al. analysed administrative data retrievable from the local government’s compliance and regulatory services [38].
Unfortunately, such large-scale open-access government datasets on housing-related ASB or neighbourhood nuisances are unavailable in Hong Kong, even in the public housing sector. The Hong Kong Housing Authority has implemented the Marking Scheme for Estate Management Enforcement since 2023 to control ASB within public rental estates in Hong Kong. However, despite the author having made several attempts to acquire statistics on the reports of misdeeds and penalties imposed under the marking scheme by the Code on Access to Information, the Housing Authority has only provided comprehensive aggregated figures, which are not helpful for a neighbourhood-level analysis.
Although data crowdsourcing from social media can serve as an alternative to government datasets, the subjective bias intrinsic to how crowdsourced data are produced makes these data, especially those about neighbourhood problems or complaints, susceptible to criticism [39]. Consequently, to conduct this empirical study, primary data were gathered by administering structured questionnaire surveys to residents residing in selected private housing communities in the territory.

3.1. Analytical Model for Empirical Study

To reiterate, this study’s key focuses are the ASB and SOC levels and how they and their relationship were affected by the pandemic. Other variables were included in the analytical model as control variables. They were identified through a thorough literature review on the determinants of housing-related ASB levels. They were screened against criteria like data availability in Hong Kong and stability for the high-rise housing setting. As advocates of the social disorganisation theory [40,41,42,43] suggest, neighbourhood structural characteristics shape neighbourhood qualities such as SOC, neighbourhood attachment, and degree of social integration and eventually determine the level of crime, social disorder, and ASB in a neighbourhood. There is ample evidence in the literature that perceived that problems in physical or social incivilities in the neighbourhood decrease when there is a higher level of neighbourhood attachment among residents [44,45,46,47,48]. Another stream of research has indicated that neighbourhoods with stronger SOC tend to be less vulnerable to neighbourhood problems [4,49,50].
The strengthening or re-creation of SOC within a neighbourhood can mitigate neighbourhood problems. This relationship can be explained in at least two ways. First, a high degree of informal social control by neighbourhood residents can result from a strong SOC [51,52], and this informal social control can, in turn, deter the occurrence of neighbourhood problems. Second, from the perspective of collectivism in neighbourhood governance, ASB or disorder control depends significantly on the collective actions of residents [53]. Nurturing SOC within a neighbourhood can cultivate a culture of cooperation and foster mutual trust among residents [54,55,56]. Consequently, SOC fosters the collective efficacy of the residents in dealing with or controlling neighbourhood problems [57]. Many studies have provided empirical evidence of the inverse or negative correlation between levels of neighbourhood disorder and collective efficacy perceived by residents [50,58,59,60,61]. A greater SOC motivates residents to be more involved in their neighbourhoods and contribute more to ASB or disorder control.
The level of ASB perceived by a resident (ASB) is thus hypothesised in this study as a function of the resident’s perceived level of SOC (SOC), the socio-demographic characteristics of income (INC), education level (EDU), age (AGE), and gender (MALE), and the housing characteristics of the length of residence (LOR) and housing tenure (OWN). These socio-demographic and housing factors are included as control variables because they are thought or proven to have some impact on the perceived ASB level. For example, a study in Belgium found that older respondents were more likely to experience a combination of unwanted behaviours and nuisance problems from their neighbours [62]; another recent study discovered that younger residents had a higher likelihood of suffering nuisance problems with their neighbours [63]. Housing tenure may also affect one’s perceived level of ASB; tenants are generally more likely to report neighbour noise nuisances than homeowners [64].
The function of ASB is mathematically expressed as follows:
ASB = α0 + α1MALE + α2AGE + α3EDU + α4INC + α5LOR + α6OWN + α7SOC + ε
where ε is the stochastic term and αi (for i = 0, 1, 2, …, 7) are the coefficients to be estimated. Model (1) identifies the determinants of ASB levels before and during the pandemic. In addition, to probe the influence of the pandemic on a resident’s perception of housing-related ASB levels and the effects of the factors of interest, the following modified exploratory model is used with a combined dataset (comprising data collected before and during the pandemic):
ASB = β0 + β1MALE + β2AGE + β3EDU + β4INC + β5LOR + β6OWN + β7SOC
+ β8PANDEMIC + β9MALE × PANDEMIC + β10AGE × PANDEMIC
+ β11EDU × PANDEMIC + β12INC × PANDEMIC + β13LOR × PANDEMIC
+ β14OWN × PANDEMIC + β15SOC × PANDEMIC + δ
where δ is the stochastic term and βj (for j = 0, 1, 2, …, 15) are the coefficients to be estimated. The variable PANDEMIC indicates whether there is any difference in the resident’s perceived level of ASB before and during the pandemic. Interaction terms are also included in Model (2) to probe whether the effects of SOC and the control variables on the perceived level of ASB are sensitive to the pandemic. Models (1) and (2) are estimated using the ordinary least squares (OLS) technique.

3.2. Operationalisation of Variables

The perceived level of ASB (ASB) within a resident’s housing community, the dependent variable, was assessed using a scale developed by Yau and Chiu in 2017 [65]. This scale is designed explicitly to evaluate the severity of ASB issues in a high-rise housing context, particularly in Hong Kong’s urban areas. To measure the perceived level of ASB, residents were asked to rate the seriousness of six broad types of ASBs in their housing communities over the past 3 months using a 4-point scale (1 = ‘not an issue at all’; 2 = ‘still tolerable’, 3 = ‘serious’; 4 = ‘very serious’). The six types of ASB are (a) ‘intimidation and harassment’; (b) ‘abusiveness of common areas’; (c) ‘graffiti, fly-posting, and vandalism’; (d) ‘uncontrolled animals’; (e) ‘littering’; and (f) ‘noise nuisances’. The overall perceived seriousness level of ASB for each resident was computed as the average score across the six aspects. A higher overall score indicated a stronger perception of ASB seriousness.
The independent variable SOC was measured using a ten-item scale adapted from the Sense of Community Index (SCI) developed by Chavis et al. in 1986 [66]. Although different scales or measuring instruments have been adopted in the literature to assess individual residents’ perceived SOC, the SCI remains a popular scale for measuring SOC because of its brevity and established reliability [67,68]. Reference was, therefore, made to the SCI in this study. Customisation of the SCI scale to fit the local context is also common and justifiable [69]. This study used ten items, as presented in Table 1, to assess the residents’ perceptions. Each resident was asked to specify their agreement or disagreement with each statement using a 5-point Likert scale (1 = ‘strongly disagree’; 2 = ‘disagree’; 3 = ‘neither agree nor disagree’; 4 = ‘agree’; 5 = ‘strongly agree’). The scores for the ten constituent items were averaged to determine each resident’s overall level of SOC, yielding a simple mean.
MALE, indicating the resident’s gender, was a dummy variable that equals one if the resident was male and zero otherwise. AGE was measured with a six-category ordinal scale representing the age group of the resident (1 = ‘18–24 years old’; 2 = ‘25–34 years old’; 3 = ‘35–44 years old’; 4 = ‘45–54 years old’; 5 = ‘55–64 years old’; 6 = ‘65 years old or above’). EDU was a five-category ordinal variable indicating the level of highest educational attainment of the resident (1 = ‘primary school or below’; 2 = ‘lower secondary school’; 3 = ‘upper secondary school or matriculation’; 4 = ‘sub-degree post-secondary education’; 5 = ‘degree or above’). INC indicated the average monthly income of the resident and was measured with a six-category ordinal variable (1 = ‘below HKD10,000’; 2 = ‘HKD10,000–19,999’; 3 = ‘HKD20,000–29,999’; 4 = ‘HKD30,000–39,999’; 5 = ‘HKD40,000–49,999’; 6 = ‘HKD50,000 or above’. LOR, measured in years, denoted how long the resident had lived in the current housing community at the time of the survey. The housing tenure of the resident was indicated by the dummy variable OWN, which equalled one if the resident was an owner-occupier and zero otherwise. PANDEMIC was a dummy variable equal to one if the resident was surveyed during the pandemic and zero otherwise.

3.3. Data Collection

The analytical framework described above was tested using data collected from two self-administered structured questionnaire surveys. One survey was conducted before the onset of the pandemic, and the other was conducted during the pandemic, focusing on residents from the same set of private housing communities in Hong Kong. The target participants for the study were selected using a comprehensive multistage sampling approach designed to ensure a representative and diverse sample. The initial phase involved the purposive selection of Tseung Kwan O, a district in Hong Kong known for its varied private housing communities. This district was chosen due to its rich diversity in residential types, taking into account critical factors such as the age of the buildings, their scale, and the socioeconomic class of the residents. The researcher aimed to capture a wide range of experiences and perspectives by focusing on such a varied environment.
Following the selection of Tseung Kwan O, the researcher compiled a detailed list of private housing communities featuring 15 stories or more residential towers. This meticulous compilation resulted in a total of 70 housing communities being identified. A random sampling method was employed to narrow down the sample for the study, ultimately selecting 17 communities. This process yielded a sampling rate of 24.3%, ensuring that the sample was statistically robust while still manageable for data collection. Table 2 shows the profile of these 17 selected private housing communities.
In the next stage, the researcher created a comprehensive register of all housing units within the selected 17 high-rise communities, encompassing around 18,700 housing units. From this extensive register, a random sample of at least 15% of the housing units in each community was chosen for data collection. This method resulted in the eventual selection of 3000 housing units. The calculated margin of error for these housing unit selections stood at 1.6%, with a confidence level of 95%. This indicates a high degree of reliability in the survey results, suggesting that they accurately reflect the opinions and characteristics of the target population. In other words, the sample obtained is expected to be representative.
Invite cards were dispatched to each of the sampled housing units to facilitate participation in the study. These cards were explicitly addressed to the heads of households, inviting them to complete an online questionnaire. To foster an environment of trust and encourage candid responses, the invitation cards clearly emphasised the voluntary and anonymous nature of participation. Additionally, each card featured a quick response (QR) code that invitees could quickly scan, providing direct access to the online questionnaire hosted on the Qualtrics web-based survey platform. This thoughtful approach not only streamlined the data collection process but also enhanced participant engagement by making it as convenient as possible to respond.
The questionnaire for the empirical study was meticulously crafted to ensure the collection of relevant data. Initially, it was developed in English to facilitate alignment with established concepts and measurement scales prevalent in the international literature, ensuring that the study could be contextualised within broader academic frameworks. The researcher employed the back-translation technique to guarantee that the translated version maintained accuracy and conceptual integrity. This process involved two key steps: first, translating the original English questionnaire into Chinese, followed by translating the Chinese version back into English. This iterative method served as a crucial quality check, allowing the researcher to verify that the meanings and nuances of the original questions were preserved in the translated version, thus achieving both conceptual and linguistic equivalence. Before launching the official survey, both the English and Chinese versions of the questionnaire were subjected to a pre-testing phase. Recognising the significance of linguistic and cultural nuances, the researcher selected ten participants for this stage: five native Cantonese speakers and five native English speakers. Their diverse linguistic backgrounds provided valuable insights into the clarity and cultural relevance of the questions. Based on the feedback from these pre-test participants, the researcher made necessary modifications and adjustments to the questionnaires, ultimately refining them into the final version that would be used in the study. This thorough process ensured that the questionnaire was accurately translated and appropriately tailored for the target population.
The data for this study were collected using the finalised questionnaires. The first survey was administered between December 2017 and April 2018. After three rounds of invitations were sent to the sampled households, 632 responses were received, for an overall response rate of 21.1%. Of these 632 responses, 592 were deemed complete and valid for inclusion in the data analysis. The second survey was conducted during the pandemic period between December 2020 and March 2021 in the same 17 housing communities using the exact sampling and survey methods. After three rounds of invitations, 485 responses were received for an overall response rate in the second survey of 16.2%. Of these 485 responses, 479 were complete and valid. Therefore, the final dataset comprised 592 valid responses from the first survey (conducted in the pre-pandemic period) and 479 from the second survey (conducted during the pandemic). The respondents in the two surveys did not necessarily overlap because another 3000 housing units were randomly sampled in the second survey.
As shown in Table 3, there were similarities in the respondents’ socio-demographic characteristics across both surveys. Males dominated the samples. The age distribution revealed that the most significant segments of respondents fell within the 45–54 years and 35–44 years age brackets. The income distributions of the respondents of the two samples were bell-shaped. Respondents with post-secondary educational attainment accounted for over 70% of both samples. The average length of residence was reported as 13.4 years with a standard deviation of 7.7 years in the first survey and 14.8 years with a standard deviation of 8.4 years in the second survey. These statistics suggest that both samples included a diverse mix of long-term residents and newer arrivals, which is essential for capturing a broad range of experiences and perspectives within the community.

4. Results

Table 4 and Table 5 report the responses’ mean values and standard deviations regarding the perceived levels of different ASB and SOC items. According to the findings from both surveys, ‘noise nuisances’ and ‘littering’ were the most serious ASB problems in the eyes of the respondents. These findings generally echo those of previous studies [4,70,71]. The overall level of ASB perceived during the pandemic was significantly higher than before the pandemic (p < 0.05 for one-tailed t-test). ‘Noise nuisances’, ‘uncontrolled animals’, and ‘abusiveness of common areas’ were the three aspects contributing to the increase in the degree of ASB perceived.
The overall perceived level of SOC also increased between the pre-pandemic and pandemic periods. The overall SOC level perceived by the respondents was significantly higher during the pandemic (p < 0.05 for one-tailed t-test). A noticeable rise in mean scores was found in the items SOC04, SOC05, SOC06, SOC07, and SOC10. These findings imply that residents in a housing community tend to be more united when facing crises such as the COVID-19 pandemic, and the longer time spent at home during the pandemic offered more opportunities for the residents to know their neighbours. The summary statistics of all variables used for the OLS regression analyses are shown in Table 6.
Table 7 presents the estimation results of the OLS regressions using the two samples and the combined sample. Model (1a) and Model (1b) represent the results returned by using the data from the first survey (conducted in the pre-pandemic period) and the second survey (conducted in the pandemic period), respectively. Model (2) covers all data from the two surveys. The adjusted R2 of the estimated models ranged from 0.36 to 0.59, signifying that the models had moderate to relatively high explanatory powers. There were similarities in the estimation results of Models (1a) and (1b). In both models, the variables OWN and SOC had a negative and significant (p < 0.05 at least) effect on the perceived level of ASB. This shows that regardless of the pandemic occurrence, renters tended to report a higher degree of ASB than owner-occupiers and residents with a stronger SOC perceived tended to indicate a lower degree of ASB within their housing communities. The results on the effect of LOR on ASB were mixed: a positive and significant (p < 0.01) relationship was found between the two variables in the pre-pandemic period, but a negative and significant (p < 0.1) correlation was found during the pandemic.
The estimation results of Model (2) were more similar to those of Model (1a) than Model (1b). In Model (2), LOR, OWN, and SOC were significant (p < 0.05 at most) determinants of ASB, with LOR showing positive effects and OWN and SOC showing negative effects. A positive and significant estimated coefficient for the dummy variable PANDEMIC was returned from the regression. This implies that with other things being constant, the perceived level of ASB was higher during the pandemic than in the pre-pandemic period. This result is in line with the result of the t-test reported in the previous section. Among the interaction terms, the estimated coefficient was significant (p < 0.05 at most) only for LOR × PANDEMIC and SOC × PANDEMIC. Both of these interaction terms showed a negative effect on ASB. The positive relationship between LOR and ASB was moderated negatively by PANDEMIC, indicating that the positive relationship between the variables in the pre-pandemic period did not hold during the pandemic period. In contrast, PANDEMIC significantly strengthened the negative relationship between SOC and ASB.
The results of the regression models suggest that none of the socio-demographic control variables significantly affected residents’ perceived ASB level. Still, the housing tenure of the residents was a significant factor. Renters or tenants perceived a higher degree of ASB in their housing communities before and during the pandemic. This finding aligns with previous studies’ empirical evidence [64,72]. Unlike owner-occupiers, tenants tend to complain more about neighbourhood problems and neighbour nuisances. Tenants are less tolerant of ASB or neighbour nuisances, perhaps because their transaction costs of relocation are usually lower than those of owner-occupiers [73].

5. Discussion

5.1. Policy Implications of the Findings

The findings from the bivariate and multivariate analyses highlight significant shifts in residents’ perceptions of ASBs and SOC in the wake of the COVID-19 pandemic, offering critical insights for policymakers. The increase in perceived ASBs can be attributed to the unique circumstances of the pandemic, where prolonged periods at home led to more frequent interactions that could disrupt neighbourly peace. For example, face-to-face teaching for schools at different levels (including kindergarten, primary, and secondary schools) was suspended and later shifted to an online mode. Under the special pandemic working arrangements, civil servants and employees of many private and public organisations worked from home. In this context, one’s behaviours were more likely to interfere with one’s neighbours. Under this circumstance, activities that were once benign, like listening to the radio, became disruptive when neighbours were working or studying from home. This suggests that urban living arrangements must be re-evaluated, particularly in high-density areas, to mitigate conflicts arising from close living quarters during such unprecedented times.
Conversely, the analysis results also reveal a nuanced picture of community resilience. The negative interaction between pandemic conditions and the length of residence suggests that the typically positive relationship between more extended residence and ASB was weakened or reversed during the pandemic, possibly indicating increased social cohesion or adaptive strategies among long-term residents. More importantly, the strengthened negative relationship between SOC and ASB during the pandemic highlights the protective role of strong community bonds in mitigating the rise in ASBs or neighbour nuisances during times of crisis. The pandemic possibly fostered opportunities for mutual support among residents, increasing SOC. In Hong Kong, despite the social distancing restrictions, the mobility of most residents was generally unrestricted during the pandemic, and they were free to move around within their housing communities or nearby. The only exception was when their communities were declared ‘restricted areas’ for compulsory testing; in this case, they were then required to stay on their premises, and government staff arranged for them to undergo compulsory testing at temporary specimen collection stations. Generally, people had more time to spend in their neighbourhoods and more opportunities to become acquainted and socially interact with their neighbours. For instance, neighbours had opportunities to see and talk with each other in the queues when lining up for compulsory testing. Besides, sharing resources, such as personal protective equipment (such as face masks and face shields) and assistance with daily necessities, underscores the potential for community solidarity during crises. Policymakers should recognise this duality and promote initiatives encouraging neighbourly interactions while addressing ASB through community-building strategies.
The negative and significant correlation between ASB and SOC indicated that residents with a stronger SOC perceived fewer ASB problems in their neighbourhoods before the COVID-19 pandemic. This result is consistent with the findings of many empirical studies [45,50,74]. Interestingly, the negative effect of SOC on perceived ASB levels persisted and was even amplified after the onset of the pandemic. To a great extent, these results confirm the relevance of the communitarian approach to housing-related ASB control, as advocated by several scholars [4,5,6]. Also, these results underscore the importance of fostering and strengthening SOC as a key component of building resilient communities capable of weathering major disruptions like pandemics.
As such, the government could devise multipronged strategies for community creation or re-creation to deal with ASB or neighbour nuisance problems at the community level. On the infrastructural side, more communal space and facilities should be incorporated into the design and planning of new housing communities to facilitate residents’ social interactions. This is particularly an issue in high-rise or high-density residential neighbourhoods that lack communal space and facilities. The significant role of communal space and facilities in creating SOC has been extensively proven [75,76,77,78,79]. For example, a 2022 study in Korea by Lee et al. found that community gardens helped to build SOC in high-density neighbourhoods [79]. Vigilance should be given to the design and planning of communal space and facilities in housing development to enhance SOC [80,81]. Moreover, communal spaces and facilities should be well maintained and managed, as otherwise, they will be of no use for promoting SOC or even become a sign of neighbourhood decline or disorder, deterring neighbourly interactions.
Furthermore, housing management agents, resident associations, and residents can devise strategies to promote SOC within their housing developments. For example, they can organise various social activities (e.g., Christmas parties, spring feasts, tea gatherings, and outings) and interest groups for residents [82]. Furthermore, to better integrate newcomers into an established housing development, it is essential to make them feel welcome. Therefore, management parties can organise induction programmes or welcoming parties for new residents. These initiatives would provide opportunities for newcomers to become acquainted with their neighbours and the community. By facilitating these welcoming activities, management parties can ensure new residents feel supported and smoothly transition into the housing development.
To support these endeavours, the government could provide funding, resources, and logistical assistance through different channels, ensuring that communities have the means to foster engagement and cohesion. For instance, the Home Affairs Department or District Councils could set up some funding schemes and manage the distribution of the resources. Beyond financial investment, exploring alternative dispute resolution mechanisms, such as mediation, is a promising approach to resolving neighbour disputes. Since many conflicts escalate when not managed promptly, implementing responsive and non-confrontational methods can preserve social ties and enhance community spirit [71]. It is crucial to embrace responsive and non-confrontational dispute resolution methods to preserve social bonds.
The proposed multipronged communitarian approach offers a sustainable and practical framework for addressing ASB problems across various housing types in high-rise cities worldwide. This approach transcends the boundaries of public and private housing, thus recognising the tenure-neutral nature of ASBs and neighbour disputes [70,83,84]. Traditional measures such as probationary tenancies and lease termination primarily target social or public housing tenants and prove ineffective for private housing residents, especially owner-occupiers, due to the protection of private property rights. Moreover, enforcing covenants, such as the deed of mutual covenant in Hong Kong, has limited success in curbing ASB as housing management agents lack the authority to penalise perpetrators [56]. Therefore, the communitarian approach, focusing on fostering SOC and promoting positive social interactions, warrants serious consideration by policymakers, housing providers, and housing management practitioners. This approach offers a more holistic and sustainable solution to address ASB problems in housing communities, ultimately creating safer and more harmonious living environments for all residents.

5.2. Limitations of the Research

In a strict sense, Hong Kong residents did not experience mandatory city lockdowns or curfews during the pandemic. Nonetheless, the mobility of particular groups of people was restricted temporarily. For example, at some points, people infected with COVID-19 and those who had been in close contact with them were required to stay in community isolation facilities or quarantine at home. It would be interesting to compare the levels of housing-related ASB problems experienced by people in various places with different degrees of mobility restrictions during the pandemic. Moreover, this study only evaluated the perceived SOC before and during the pandemic. As the possible long-term effects of the pandemic on SOC are unknown, it would be worthwhile to explore how long the positive impacts of the pandemic on fostering SOC have been sustained.

6. Conclusions

6.1. Summary and Contributions of the Research

The COVID-19 pandemic provided a novel context for investigating the effects of SOC on housing-related ASB. During the pandemic, people spent more time at home due to mobility restriction policies and adopting work-from-home and remote learning practices. This had positive and negative effects. On the one hand, the likelihood of experiencing nuisances or disturbances increased because more people spent more time in their housing community. More frequent mutual interference among residents can be expected under these circumstances. On the other hand, more time spent in the housing community means more chances for neighbourly interactions. Furthermore, the pandemic opened up opportunities for mutual aid and support, which could help residents build relationships with each other and develop social cohesion, thus fostering their SOC. Drawing on the survey findings in Hong Kong, this study discovered a notable and inverse correlation between residents’ perceived levels of ASB seriousness and SOC within their housing communities before and during the pandemic. Revealing how the COVID-19 pandemic has reshaped residents’ perceptions of ASBs and SOC is a significant academic contribution. Through rigorous bivariate and multivariate analyses, the research underscores the protective role of strong community bonds in mitigating ASBs during crises, suggesting that fostering SOC is essential for building resilient urban communities. This nuanced understanding of the relationship between ASBs and SOC during unprecedented times offers valuable insights for both scholars and practitioners in urban studies and community development.
In addition, the broader policy implications of these findings are profound. The research findings affirm the significance of adopting a multipronged communitarian approach to address ASB issues in housing communities. Cultivating a SOC among all established and new residents can contribute to more harmonious living environments. The insights gleaned from this study could extend beyond Hong Kong and benefit other high-rise, high-density cities, particularly those in Asia. Policymakers of these cities are encouraged to re-evaluate urban planning and governance strategies, especially in high-density living environments, to reduce conflicts among residents. The study advocates for creating more communal spaces and organising community-building initiatives, which can enhance social interactions and the integration of new residents. Furthermore, implementing alternative dispute resolution mechanisms can help maintain social ties and community spirit.

6.2. Agenda for Further Studies

The current research focuses on one particular Asian city, namely Hong Kong. It will be valuable to conduct comparative studies across different high-density urban areas or cities, from the East and the West, in the future to allow for a broader contextual understanding of the current research findings. Additionally, integrating qualitative insights or case studies could enrich the analysis, providing a more nuanced perspective that complements the quantitative data. Future research will also benefit from including public rental housing residents because 30.1% of the domestic households in Hong Kong lived in public rental housing as of the third quarter of 2024 [85]. Such inclusion will broaden the scope of the research findings and allow for a more comprehensive understanding of urban dynamics.

Funding

This research was funded by the Research Grants Council of the Hong Kong Special Administrative Region, China project number LU 11609220 and City University of Hong Kong project number 7004949.

Institutional Review Board Statement

Approvals for ethical review were obtained from the Human Subjects Ethics Sub-Committee, City University of Hong Kong (Reference Nos.: 2-6-201709_02 on 22 September 2017 and 2-84-202003-02 on 27 March 2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to confidentiality concerns.

Conflicts of Interest

The author declares no conflicts of interest.

References

  1. Felder, M. Strong, weak and invisible ties: A relational perspective on urban coexistence. Sociology 2020, 54, 675–692. [Google Scholar] [CrossRef]
  2. Mazerolle, L.; Wickes, R.; McBroom, J. Community variations in violence: The role of social ties and collective efficacy in comparative context. J. Res. Crime Delinq. 2010, 47, 3–30. [Google Scholar] [CrossRef]
  3. Ross, C.E.; Jang, S.J. Neighborhood disorder, fear, and mistrust: The buffering role of social ties with neighbors. Am. J. Community Psychol. 2000, 28, 401–420. [Google Scholar] [CrossRef] [PubMed]
  4. Yau, Y. Anti-social behaviour management: A communitarian approach. Habitat Int. 2014, 42, 245–252. [Google Scholar] [CrossRef]
  5. Campbell, A.; Hughes, J.; Hewstone, M.; Cairns, E. Social capital as a mechanism for building a sustainable society in Northern Ireland. Community Dev. J. 2010, 45, 22–38. [Google Scholar] [CrossRef]
  6. Hopkins-Burke, R.; Hodgson, P. Anti-social behaviour, community and radical moral communitarianism. Cogent Soc. Sci. 2015, 1, 1033369. [Google Scholar] [CrossRef]
  7. Birer, E.; Hasgül, E.; Can, E. Transformation of home environments in pandemic: The concept of “life fits into room” in the new normal. Open House Int. 2022, 47, 87–106. [Google Scholar] [CrossRef]
  8. Jones, M.; Beardmore, A.; Biddle, M.; Gibson, A.; Ismail, S.U.; McClean, S.; White, J. Apart but not alone? A cross-sectional study of neighbour support in a major UK urban area during the COVID-19 lockdown. Emerald Open Res. 2023, 1, 37. [Google Scholar] [CrossRef]
  9. Stevenson, C.; Wakefield, J.R.H.; Felsner, I.; Drury, J.; Costa, S. Collectively coping with coronavirus: Local community identification predicts giving support and lockdown adherence during the COVID-19 pandemic. Br. J. Soc. Psychol. 2021, 60, 1403–1418. [Google Scholar] [CrossRef] [PubMed]
  10. Basaure, M.; Joignant, A.; Mascareño, A. Between distancing and interdependence: The conflict of solidarities in the COVID-19 pandemic. Sociol. Perspect. 2021, 64, 706–725. [Google Scholar] [CrossRef]
  11. Ramphal, B.; Dowkin, J.D.; Pagliaccio, D.; Margolis, A.E. Noise complaint patterns in New York City from January 2010 through February 2021: Socioeconomic disparities and COVID-19 exacerbations. Environ. Res. 2022, 206, 112254. [Google Scholar] [CrossRef] [PubMed]
  12. Bartalucci, C.; Bellomini, R.; Luzzi, S.; Pulella, P.; Iannuzzi, G.; Torelli, G. A renewed perception of living environments after restrictions due to pandemic in Italy. Noise Mapp. 2022, 9, 189–203. [Google Scholar] [CrossRef]
  13. Kleeman, A.; Foster, S. ‘It feels smaller now’: The impact of the COVID-19 lockdown on apartment residents and their living environment—A longitudinal study. J. Environ. Psychol. 2023, 89, 102056. [Google Scholar] [CrossRef] [PubMed]
  14. Erin, İ.; Çubukçu, E. Effects of COVID-19 pandemic on neighbourhood perception and satisfaction. Kent Akad. Derg. 2022, 15, 19–37. [Google Scholar] [CrossRef]
  15. Helliwell, J.F.; Putnam, R.D. The social context of well-being. Philos. Trans. R. Soc. Lond. Biol. Sci. 2004, 359, 1435–1446. [Google Scholar] [CrossRef] [PubMed]
  16. Greenfield, E.A.; Reyes, L. Continuity and change in relationship with neighbors: Implications for psychological well-being in middle and later life. J. Gerontol. Ser. B 2015, 70, 607–618. [Google Scholar] [CrossRef]
  17. Kaniasty, K. Social support, interpersonal, and community dynamics following disasters caused by natural hazards. Curr. Opin. Psychol. 2020, 32, 105–109. [Google Scholar] [CrossRef]
  18. LaLone, M.B. Neighbors helping neighbors: An examination of the social capital mobilization process for community resilience to environmental disasters. J. Appl. Soc. Sci. 2012, 6, 209–237. [Google Scholar] [CrossRef]
  19. Toya, H.; Skidmore, M. Do natural disasters enhance societal trust. Kyklos 2013, 67, 255–279. [Google Scholar] [CrossRef]
  20. Gatti, F.; Procentese, F. Local community experience as an anchor sustaining reorientation processes during COVID-19 pandemic. Sustainability 2021, 13, 4385. [Google Scholar] [CrossRef]
  21. Zangger, C. Localized social capital in action: How neighborhood relations buffered the negative impact of COVID-19 on subjective well-being and trust. SSM—Popul. Health 2023, 21, 101307. [Google Scholar] [CrossRef] [PubMed]
  22. Zetterberg, L.; Santosa, A.; Ng, N.; Karlsson, M.; Eriksson, M. Impact of COVID-19 on neighborhood social support and social interactions in Umeå municipality, Sweden. Front. Sustain. Cities 2021, 3, 685737. [Google Scholar] [CrossRef]
  23. Silverio-Murillo, A.; Hoehn-Velasco, L.; Balmori de la Miyar, J.R. Disputes between neighbors in Mexican cities during the COVID-19 pandemic. Reg. Sci. Policy Pract. 2023, 15, 626–644. [Google Scholar] [CrossRef]
  24. Yildirim, Y.; Arefi, M. Noise complaints during a pandemic: A longitudinal analysis. Noise Mapp. 2021, 8, 108–115. [Google Scholar] [CrossRef]
  25. Lee, P.J.; Jeong, J.H. Attitudes towards outdoor and neighbour noise during the COVID-19 lockdown: A case study in London. Sustain. Cities Soc. 2021, 67, 102768. [Google Scholar] [CrossRef] [PubMed]
  26. Tong, H.; Aletta, F.; Mitchell, A.; Oberman, T.; Kang, J. Increases in noise complaints during the COVID-19 lockdown in spring 2020: A case study in greater London, UK. Sci. Total Environ. 2021, 785, 147213. [Google Scholar] [CrossRef]
  27. Bartalucci, C.; Bellomini, R.; Luzzi, S.; Pulella, P.; Iannuzzi, G.; Torelli, G. A survey on the soundscape perception before and during the COVID-19 pandemic in Italy. Noise Mapp. 2021, 8, 65–88. [Google Scholar] [CrossRef]
  28. Yildirim, Y.; Kesharvarzi, G.; Arefi, M. Noise complaints, the COVID-19 pandemic, and compact developments: Evidence from five American cities. Environ. Sci. Pollut. Res. 2023, 30, 40724–40736. [Google Scholar] [CrossRef] [PubMed]
  29. Dümen, A.S.; Şaher, K. Noise annoyance during COVID-19 lockdown: A research of public opinion before and during the pandemic. J. Acoust. Soc. Am. 2020, 148, 3489–3496. [Google Scholar] [CrossRef]
  30. Holling, C.S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef]
  31. Luthar, S.; Cicchetti, D.; Becker, B. The construct of resilience: A critical evaluation and guidelines for future work. Child Dev. 2000, 71, 543–562. [Google Scholar] [CrossRef] [PubMed]
  32. Salzarulo, A.; Mundorf, N.; Sakar, J.; Terui, M.; Lei, W. Communication as a tool for empowerment: A model for resilience. China Media Res. 2015, 11, 78–87. [Google Scholar]
  33. Xu, L.; Guo, X.; Gollagher, M.; Marinova, D. Social networks, community resilience, and bonding relationships. In Sustainability and Development in Asia and the Pacific: Emerging Policy Issues; Guo, X., Marinova, D., Eds.; World Scientific Publishing: Singapore, 2018; pp. 103–123. [Google Scholar]
  34. den Broeder, L.; South, J.; Rothoff, A.; Bagnall, A.-M.; Azarhoosh, F.; van der Linden, G.; Bharadwa, M.; Wagemakers, A. Community engagement in deprived neighbourhoods during the COVID-19 crisis: Perspectives for more resilient and healthier communities. Health Promot. Int. 2022, 37, daab098. [Google Scholar] [CrossRef] [PubMed]
  35. Malone-Lee, L.-C. The ecology of neighbourhood resilience: A multi-disciplinary perspective. In Building Resilient Neighbourhoods in Singapore: The Convergence of Policies, Research and Practice; Leong, C.-H., Malone-Lee, L.-C., Eds.; Springer: Singapore, 2020; pp. 1–11. [Google Scholar]
  36. Zhang, X.; Luo, Y.; Liu, Y.; Han, Z.; Wang, F. Resilience in urban, rural, and transitional communities: An empirical study in Guangdong, China. Int. J. Disaster Risk Reduct. 2023, 84, 103396. [Google Scholar] [CrossRef]
  37. Lara-Hernandez, J.A.; Chin, J.J.-Y. Sense of community and spatial agency: Key elements of resilient communities. In Resilient Communities and the Peccioli Charter: Towards the Possibility of an Italian Charter for Resilient Communities; Carta, M., Perbellini, M.R., Lara-Hernandez, J.A., Eds.; Springer: Cham, Switzerland, 2022; pp. 59–64. [Google Scholar]
  38. Liu, Y.; Cheshire, L.; Wang, S.; Fu, X. A socio-spatial analysis of neighbour complaints using large-scale administrative data: The case in Brisbane, Australia. Cities 2019, 90, 168–180. [Google Scholar] [CrossRef]
  39. Solymosi, R.; Bowers, K.J.; Fujiya, T. Crowdsourcing subjective perceptions of neighbourhood disorder: Interpreting bias in open data. Br. J. Criminol. 2018, 58, 944–967. [Google Scholar] [CrossRef]
  40. Yoon, M. Neighborhood structural characteristics, perceived neighborhood environment, and problem behaviors among at-risk adolescents. J. Community Psychol. 2021, 49, 2639–2657. [Google Scholar] [CrossRef] [PubMed]
  41. Shaw, C.R.; McKay, H.D. Juvenile Delinquency and Urban Areas; University of Chicago Press: Chicago, IL, USA, 1942. [Google Scholar]
  42. Park, R.E.; Burgess, E.W.; McKenzie, R.D. The City; University of Chicago Press: Chicago, IL, USA, 1967. [Google Scholar]
  43. Cantillon, D.; Davidson, W.S.; Schweitzer, J.H. Measuring community social organization: Sense of community as a mediator in social disorganization theory. J. Crim. Justice 2003, 31, 321–339. [Google Scholar] [CrossRef]
  44. Zahnow, R.; Tsai, A. Crime victimization, place attachment, and the moderating role of neighborhood social ties and neighboring behavior. Environ. Behav. 2021, 53, 40–68. [Google Scholar] [CrossRef]
  45. Pitner, R.O.; Yu, M.; Brown, E. Making neighborhoods safer: Examining predictors of residents’ concerns about neighborhood safety. J. Environ. Psychol. 2012, 32, 43–49. [Google Scholar] [CrossRef]
  46. Brown, B.; Perkins, D.; Brown, G. Place attachment in a revitalizing neighbourhood: Individual and block levels of analysis. J. Environ. Psychol. 2003, 23, 259–271. [Google Scholar] [CrossRef]
  47. Brown, B.; Perkins, D.; Brown, G. Crime, new housing, and housing incivilities in a first-ring suburb: Multilevel relationships across time. Hous. Policy Debate 2004, 15, 301–343. [Google Scholar] [CrossRef]
  48. Comstock, N.; Dickinson, L.M.; Marshall, J.A.; Soobader, M.; Turbin, M.S.; Buchenaum, M.; Litt, J.S. Neighborhood attachment and its correlates: Exploring neighborhood conditions, collective efficacy, and gardening. J. Environ. Psychol. 2010, 30, 435–442. [Google Scholar] [CrossRef]
  49. Kim, J.W.; Schweitzer, J.H.; Kim, M. The relationship of community characteristics to actual and perceived crime in urban neighborhoods. Int. J. Urban Sci. 2002, 6, 118–130. [Google Scholar] [CrossRef]
  50. Lindblad, M.R.; Manturuk, K.R.; Quercia, R.G. Sense of community and informal social control among lower income households: The role of homeownership and collective efficacy in reducing subjective neighborhood crime and disorder. Am. J. Community Psychol. 2013, 51, 123–139. [Google Scholar] [CrossRef] [PubMed]
  51. Cohrun, S.E. Understanding and enhancing neighborhood sense of community. J. Plan. Lit. 1994, 9, 92–99. [Google Scholar] [CrossRef]
  52. Farahani, L.M. The value of the sense of community and neighbouring. Hous. Theory Soc. 2016, 33, 357–376. [Google Scholar] [CrossRef]
  53. Craw, M. Institutional analysis of neighborhood collective action. Public Adm. Rev. 2017, 77, 707–717. [Google Scholar] [CrossRef]
  54. Yau, Y. Sense of community and homeowner participation in housing management: A study of Hong Kong. Urbani Izziv 2010, 21, 126–135. [Google Scholar] [CrossRef]
  55. Chavis, D.M.; Wandersman, A. Sense of community in the urban environment: A catalyst for participation and community development. Am. J. Community Psychol. 1990, 18, 55–81. [Google Scholar] [CrossRef]
  56. Perkins, D.D.; Florin, P.; Rich, R.C.; Wandersman, A.; Chavis, D.M. Participation and the social and physical environment of residential blocks: Crime and community context. Am. J. Community Psychol. 1990, 18, 83–115. [Google Scholar] [CrossRef]
  57. Walton, E. “It’s not just a bunch of buildings”: Social psychological investment, sense of community, and collective efficacy in a multiethnic low-income neighborhood. City Community 2016, 15, 231–263. [Google Scholar] [CrossRef]
  58. Hipp, J.R. Collective efficacy: How is it conceptualized, how is it measured, and does it really matter for understanding perceived neighborhood crime and disorder? J. Crim. Justice 2016, 46, 32–44. [Google Scholar] [CrossRef] [PubMed]
  59. Gearhart, M.C. Preventing neighborhood disorder: Comparing alternative models of collective efficacy theory using structural equation modelling. Am. J. Community Psychol. 2019, 63, 168–178. [Google Scholar] [CrossRef]
  60. Wells, W.; Schafer, J.; Varano, S.; Bynum, T. Neighborhood residents’ production of order: The effects of collective efficacy on responses to neighborhood problems. Crime Delinq. 2006, 52, 523–550. [Google Scholar] [CrossRef]
  61. Sampson, R.J. Neighborhood and community: Collective efficacy and community safety. New Econ. 2004, 11, 106–113. [Google Scholar] [CrossRef]
  62. Michaux, E.; Groenen, A.; Uzieblo, K. Unwanted behaviors and nuisance behaviors among neighbors in a Belgian community sample. J. Interpers. Violence 2017, 32, 1967–1994. [Google Scholar] [CrossRef]
  63. Zahnow, R.; Cheshire, L. Community neighboring norms and the prevalence and management of private neighbor problems. City Community 2023, 22, 126–144. [Google Scholar] [CrossRef]
  64. Pekkonen, M.; Haverinen-Shaughnessy, U. Housing satisfaction in Finland with regard to area, dwelling type and tenure status. Cent. Eur. J. Public Health 2015, 23, 314–420. [Google Scholar] [CrossRef] [PubMed]
  65. Yau, Y.; Chiu, S.M. Antisocial behavior and resident participation in housing management: A study in Hong Kong’s private housing sector. In Antisocial Behavior: Perspectives, Management Strategies and Outcomes; Dobson, A., Talbot, P., Eds.; Nova Science: New York, NY, USA, 2017; pp. 31–49. [Google Scholar]
  66. Chavis, D.M.; Hogge, J.H.; McMillan, W.D.; Wandersman, A. Sense of community through Brunswick’s lens: A first look. J. Community Psychol. 1986, 14, 24–40. [Google Scholar] [CrossRef]
  67. Dong, X.; Chang, E.; Simon, M.A. Sense of community among Chinese older adults in the Greater Chicago Area: Findings from the PINE study. AIMS Med. Sci. 2014, 1, 28–39. [Google Scholar] [CrossRef]
  68. Wilson-Doenges, G. An exploration of sense of community and fear of crime in gated communities. Environ. Behav. 2000, 32, 597–611. [Google Scholar] [CrossRef]
  69. Yu, R.; Wong, M.; Woo, J. Perceptions of neighborhood environment, sense of community, and self-rated health: An age-friendly city project in Hong Kong. J. Urban Health 2019, 96, 276–288. [Google Scholar] [CrossRef] [PubMed]
  70. Yau, Y. Insignificant or ignored? Antisocial behaviour in private housing in Hong Kong. Urbani Izziv 2012, 23, 103–111. [Google Scholar] [CrossRef]
  71. Yau, Y. Ruling out trouble: Unacceptable behaviour and its control in Hong Kong’s public housing. Habitat Int. 2012, 36, 11–19. [Google Scholar] [CrossRef] [PubMed]
  72. Ellaway, A.; Macintyre, S. Does housing tenure predict health in the UK because it exposes people to different levels of housing related hazards in the home or its surroundings? Health Place 1998, 4, 141–150. [Google Scholar] [CrossRef] [PubMed]
  73. Tong, H.; Kang, J. Relation between noise complaints and socio-economic factors in England. Sustain. Cities Soc. 2021, 65, 102573. [Google Scholar] [CrossRef]
  74. Ayoola, A.H. Neighbourhood characteristics and their relationship to sense of community in poor residential neighbourhoods in Akure, Ondo State, Nigeria. J. Urban Regen. Renew. 2018, 11, 278–292. [Google Scholar] [CrossRef]
  75. Li, L.H. The physical environment and a “sense of neighborhood” in residential communities in Hong Kong. Prop. Manag. 2008, 26, 7–24. [Google Scholar]
  76. Marcus, C.C. Site planning, building design and a sense of community: An analysis of six cohousing schemes in Denmark, Sweden, and the Netherlands. J. Archit. Plan. Res. 2000, 17, 146–163. [Google Scholar]
  77. Zhang, T.; Liu, J.; Chen, H.; Ng, M.K. The associations of communal space with sense of place and mental health in public housing: Evidence from Guangzhou and Hong Kong. Int. J. Environ. Res. Public Health 2022, 19, 16178. [Google Scholar] [CrossRef]
  78. Ewen, C.; Warner, E.; Andrews, F.J. Communal spaces in apartment complexes in Melbourne, Australia: Designs to foster social connectedness. Cities Health 2023, 7, 363–377. [Google Scholar] [CrossRef]
  79. Lee, S.; Jang, H.; Yun, H.; Jung, Y.; Hong, I. Effect of apartment community garden program on sense of community and stress. Int. J. Environ. Res. Public Health 2022, 19, 708. [Google Scholar] [CrossRef]
  80. Wu, W.; Ge, X.J. Communal space design of high-rise apartments: A literature review. J. Des. Built Environ. 2020, 20, 35–49. [Google Scholar] [CrossRef]
  81. Kleeman, A.; Giles-Corti, B.; Gunn, L.; Hooper, P.; Foster, S. The impact of the design and quality of communal areas in apartment buildings on residents’ neighbouring and loneliness. Cities 2023, 133, 104126. [Google Scholar] [CrossRef]
  82. Rosenblatt, T.; Chehsire, L.; Lawrence, G. Social interaction and sense of community in a master planned community. Hous. Theory Soc. 2009, 26, 122–142. [Google Scholar] [CrossRef]
  83. Flint, J.; Nixon, J. Governing neighbours: Anti-social behaviour orders and new forms of regulating conduct in the UK. Urban Stud. 2006, 43, 939–955. [Google Scholar] [CrossRef]
  84. Vols, M.; Fick, S. Using eviction to combat housing-related crime and anti-social behaviour in South Africa and the Netherlands. S. Afr. Law J. 2017, 134, 327–360. [Google Scholar]
  85. Table 130-06603: Domestic Households by Type of Housing. Available online: https://www.censtatd.gov.hk/en/web_table.html?id=130-06603 (accessed on 30 December 2024).
Table 1. Measurement of SOC (adapted from [66]).
Table 1. Measurement of SOC (adapted from [66]).
ItemStatement
SOC01I think my housing community is a good place for me to live.
SOC02I expect to live in this housing community for a long time.
SOC03It is very important to me to live in this particular housing community.
SOC04People in this housing community share the same values and have similar needs, priorities, and goals.
SOC05Many of my neighbours know me.
SOC06I can recognise most of the people who live on my block.
SOC07I have influence over what this housing community is like.
SOC08People on this block generally get along well with each other and care about each other.
SOC09I trust the people living in this housing community.
SOC10If there is a problem in this housing community, people who live here can get it solved.
Table 2. Profile of the private housing communities sampled for the two surveys.
Table 2. Profile of the private housing communities sampled for the two surveys.
CharacteristicMinimumMeanMaximumσ
Total number of housing units3721488.257261300.2
Age when the second survey was conducted (Years)516.6299.4
Percentage of renters (%)2.115.532.38.7
Table 3. Profile of the valid respondents in the surveys.
Table 3. Profile of the valid respondents in the surveys.
CharacteristicPercentage (%)
1st Survey
(n = 592)		2nd Survey
(n = 479)
GenderMale70.972.2
Female29.127.8
Age18–24 years old4.12.5
25–34 years old11.112.6
35–44 years old27.531.1
45–54 years old39.235.8
55–64 years old13.911.1
65 years old or above4.26.9
IncomeHKD 9999 or below1.00.7
HKD 10,000–19,9999.05.9
HKD 20,000–29,99919.923.1
HKD 30,000–39,99939.537.3
HKD 40,000–49,99923.023.8
HKD 50,000 or above7.69.2
Education levelPrimary school or below3.22.5
Lower secondary school8.76.0
Upper secondary school or matriculation18.920.2
Sub-degree post-secondary education36.837.1
Degree or above32.434.2
Housing tenureOwner-occupier77.479.5
Renter22.620.5
Table 4. Levels of ASB perceived by the respondents.
Table 4. Levels of ASB perceived by the respondents.
Type of ASB1st Survey (n = 592)2nd Survey (n = 479)Overall (n = 1071)
MeanσMeanσMeanσ
Intimidation and harassment2.520.902.510.912.520.90
Abusiveness of common areas2.590.912.650.902.620.91
Graffiti, fly-posting, and vandalism2.580.912.580.902.580.92
Uncontrolled animals2.540.922.630.912.580.91
Littering2.740.942.780.952.760.95
Noise nuisances2.770.922.890.912.820.92
Overall2.620.402.670.442.650.42
Table 5. Levels of SOC perceived by the respondents.
Table 5. Levels of SOC perceived by the respondents.
SOC Item1st Survey (n = 592)2nd Survey (n = 479)Overall (n = 1071)
MeanσMeanσMeanσ
SOC012.941.032.941.022.941.02
SOC022.971.072.991.052.981.06
SOC032.910.972.930.962.920.97
SOC042.891.132.981.082.931.11
SOC052.801.132.931.092.861.11
SOC062.941.163.021.152.981.15
SOC072.771.022.861.032.811.03
SOC082.811.022.841.022.831.02
SOC092.941.102.971.112.951.11
SOC102.911.122.971.092.941.11
Overall2.890.602.940.612.910.60
Table 6. Summary statistics of the variables for the OLS regressions.
Table 6. Summary statistics of the variables for the OLS regressions.
StatisticsABSMALEAGEEDUINCLOROWNSOCPANDEMIC
1st Survey (n = 592)
Maximum3.711.006.005.006.0026.001.004.20–
Mean2.620.633.472.883.4213.420.772.89–
Minimum1.430.001.001.001.001.000.001.50–
σ0.400.481.071.301.347.670.420.60–
2nd Survey (n = 479)
Maximum3.861.006.005.006.0029.001.004.20–
Mean2.670.623.502.883.4414.790.802.97–
Minimum1.430.001.001.001.001.000.001.00–
σ0.440.491.071.301.328.390.041.09–
Overall (n = 1071)
Maximum3.861.006.005.006.0029.001.004.201.00
Mean2.650.633.492.883.4314.030.782.910.45
Minimum1.430.001.001.001.001.000.001.500.00
σ0.420.481.071.301.338.030.410.600.50
Table 7. Estimation results of OLS regressions.
Table 7. Estimation results of OLS regressions.
VariableCoefficient
Model (1a)Model (1b)Model (2)
Constant2.3930 ***4.0755 ***2.3821 ***
MALE−0.0233 −0.0500 −0.0233
AGE0.0057 0.0008 0.0057
EDU0.0116 0.0180 0.0116
INC−0.0059 −0.0190 −0.0059
LOR0.0357 ***−0.0037 *0.0356 ***
OWN−0.1989 ***−0.2181 ***−0.1989 ***
SOC−0.0428 **−0.3887 ***−0.0428 **
PANDEMIC– – 1.6825 ***
MALE × PANDEMIC– – −0.0267
AGE × PANDEMIC– – −0.0050
EDU × PANDEMIC– – 0.0064
INC × PANDEMIC– – −0.0131
LOR × PANDEMIC– – −0.0394 ***
OWN × PANDEMIC– – −0.0192
SOC × PANDEMIC– – −0.3459 **
Dependent variableASB ASB ASB
Number of observations592 479 1071
R20.4794 0.5924 0.3650
Adjusted R20.4720 0.5876 0.3555
F-statistics64.7630 ***121.2752 ***38.6718 ***
*** p < 0.01; ** p < 0.05; and * p < 0.1.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yau, Y. Enjoying Your Neighbourhood During the COVID-19 Pandemic? A Hong Kong Study on Housing-Related Anti-Social Behaviour. Buildings 2025, 15, 342. https://doi.org/10.3390/buildings15030342

AMA Style

Yau Y. Enjoying Your Neighbourhood During the COVID-19 Pandemic? A Hong Kong Study on Housing-Related Anti-Social Behaviour. Buildings. 2025; 15(3):342. https://doi.org/10.3390/buildings15030342

Chicago/Turabian Style

Yau, Yung. 2025. "Enjoying Your Neighbourhood During the COVID-19 Pandemic? A Hong Kong Study on Housing-Related Anti-Social Behaviour" Buildings 15, no. 3: 342. https://doi.org/10.3390/buildings15030342

APA Style

Yau, Y. (2025). Enjoying Your Neighbourhood During the COVID-19 Pandemic? A Hong Kong Study on Housing-Related Anti-Social Behaviour. Buildings, 15(3), 342. https://doi.org/10.3390/buildings15030342

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop