Spatial segregation and social exclusion, caused by rapidly growing global population pressures and the wealth gap, are driving unprecedented changes in social systems [1
]. Socioeconomic status inequities are growing both within and among societies [2
] and have become an indisputable reality in human settlements, especially in cities [4
]. The rate of new housing construction has lagged far behind population growth in urban centres, and the gap between high housing prices and low affordability has led to growing migration to the outskirts of many burgeoning cities [5
]. Housing costs have a significant impact on access to adequate and affordable housing, particularly for vulnerable groups [6
]. In the context of the Sustainable Development Goals, the provision of equitable housing and infrastructure in settlements is fundamental to social equity [7
]. Therefore, a redirection towards sustainability and well-being, which achieves the progressive realisation of the right to adequate housing, has been regarded as the most viable option for further development.
The spatial distribution, supply and demand of urban public service facilities are vital factors that affect the residents’ well-being [8
]. Public services, as non-competitive public goods provided by the government, could bring economic benefits. When such economic benefits persist, they will enter asset prices and be influenced by the real estate market. This capitalisation effect represents a significant increase in the value of nearby housing as a result of investment in public services. Public services, such as cultural services, healthcare, eldercare and public transportation, could serve as catalysts to stimulate surrounding real estate development [11
]. Public service facilities of superior quality in urban centres could stimulate residents’ willingness to buy houses and form a cluster of advantaged groups, thus attracting more public investment and providing better services [12
]. However, imbalanced urbanisation and fragmented local government structures may cause concentralised patterns and spatial differences in public service provision [13
]. These are capitalised to varying degrees, and thus affect housing price elasticities [15
]. In addition, as residents are willing to pay more for better access to high-quality public services, space for high housing prices is likely to cluster together, and this distribution may offset the expected incentive effects of some policies [16
]. For example, the simplistic educational policies pursuing equity, such as ‘nearby enrolment’ and ‘zero school choice’ policies [18
], cannot achieve true equity, but rather reinforce the school district effect and aggravate inequities in neighbourhoods and educational opportunities [3
]. In general, the spatial effect of accessibility to public service facilities on housing prices is not yet recognised.
In addition, the causes of urban housing inequity could be explained by the dual mechanisms of the emerging housing market and the persisting socialist institution [19
] (Figure 1
). Prior to the economic reform and opening-up in 1978, China’s urban housing system was a welfare system that relied on unified national construction and low rent distribution [21
]. This system had a strong constraint on urban spatial layout and social differentiation. After the economic reform and opening-up, China’s urban housing reform transformed access to housing from a socialist administrative allocation system to a more market-oriented housing development and consumption system [20
]. The abolition of welfare housing policy provision in 1998 was a paramount milestone in Chinese urban housing reform, which shaped a market-oriented urban housing provision system [24
]. Since then, the goal of housing commercialisation has provided Chinese urban households with the opportunity to choose their suitable houses and living environments [19
]. Individuals with higher political status, better socioeconomic conditions and the possession of organisational resources and power were more likely to have access to superior living conditions [25
]. The combined action of power and the market accelerates the division of urban housing space and gives way to the stratification process of housing space [27
]. Accordingly, this historical process not only reveals China’s economic transformation and massive urbanisation process, but also affects residents’ well-being.
This study aims to determine the spatial effects of accessibility to public service facilities on housing prices. Specifically, this paper attempts to answer three interrelated research questions: (i) Is there a significant spatial heterogeneity in the accessibility of different public service facilities? (ii) How does the accessibility of different public service facilities affect housing prices? (iii) What are the implications of the research results for promoting housing equity? To effectively engage with these research questions, the pattern of public service facilities is portrayed by road network analysis and hotspot analysis. The relationship between the accessibility of public service facilities and housing prices is investigated through the hedonic price model, geographical detector model and the spatial association detector model. The issue of housing equity is then discussed for sustainable urban planning.
5.1. The Heterogeneous Capitalisation Effect of Housing Prices
In terms of urban spatial resource allocation, various types of public services, such as recreational, medical, educational and financial facilities, all had capitalisation effects on housing prices based on the spatial association detector model. Recreational facilities closely related to daily life could meet the basic needs of residents’ lives and represent the convenience of regional life. The fast-paced life has led residents to demand more convenience from restaurants, bars and convenience stores, boosting housing prices. The construction of medical facilities such as hospitals and clinics not only has an essential impact on residents’ health and well-being, but also contributes to land development intensity, which results in increased property values in the vicinity. Educational facilities have a positive effect on the promotion of real estate and the increase in housing prices. The capitalisation effect of primary and middle schools is significantly higher than that of kindergartens, taking into account the ‘nearby enrolment’ and ‘zero school choice’ policies, and such findings are similar to those of Wen et al. [73
]. In addition, evidence indicates that the capitalisation effect of ATMs is more pronounced than that of banks, and that financial facilities significantly contribute to forming the distribution pattern of housing prices.
The traffic condition is also regarded as one of the most critical factors affecting housing prices [54
]. The construction of the metro not only changes urban land use and promotes land development intensity, but also improves the accessibility of surrounding properties to various urban services [47
]. To a certain extent, it could reduce the time cost of residents’ travel and living, thus affecting the surrounding housing prices. The main reasons why the distance to the nearest metro station has significantly higher explanatory power for housing prices than the distance to the nearest bus station are the scarcity of metro stations and their significant increase in accessibility. The number of plies also plays an essential role in housing prices. In addition, there is also a spatial spill-over effect on housing prices for environmental elements such as rivers/lakes, parks and industrial land, as well as PM2.5
and ozone pollutants. Inhabitants are more willing to pay a higher price for comfortable environmental conditions due to the increasing importance residents place on their quality of life [26
This study also revealed that the interaction effect between each pair of driving factors manifested itself as a bivariate enhancement or nonlinear enhancement affecting housing prices. Looking at our findings from another perspective, we could also speculate that the interaction between the two driving factors strengthened the effect of each on housing prices, and therefore urban planners could pay attention to multiple driving factors to promote a rational distribution of public services and housing equity.
5.2. Contributions and Limitations
Theoretically, we proposed an integrated framework to explain why and how the accessibility of multiple public services at different levels affects housing prices. Location attributes, housing attributes and environmental attributes were considered simultaneously, thus renewing knowledge about the heterogeneous capitalisation effect of public service accessibility on housing prices. It provides policy implications for the reasonable allocation of public services and housing equity. Methodologically, in contrast to previous studies that have used multiple regression models to explore the determinants of housing prices, this study applied the geographical detector model and the spatial association detector model to identify the individual and interactive effects of factors on housing prices. Particularly, this study confirmed that the hedonic price model, geographical detector model and the spatial association detector model could be valuable tools for examining differential impacts and interactions between the factors involved in housing prices, as the methodology is relatively simple and easy to implement. The methodology is not limited by geographical location and is flexible enough to be replicated and applied to other urban areas in both developed and developing countries.
However, several limitations should be mentioned in this study. First, housing authorities only keep records of housing prices for second-hand housing transactions. There are no official statistical data on rental housing or housing rental prices. Second, accessibility is not only related to the quantity of public service facilities, but also to their quality. Future research could serve housing equity by considering both simultaneously. Third, it is necessary to describe how public service facility land is provided in light of the information from the urban plans and land use plans to help understand the underlying logic of public service facility provision. Finally, the COVID-19 pandemic has significantly affected the daily commuting behaviours of urban residents. It would be valuable to monitor the changing housing rental prices, in order to examine whether the impact on housing rental prices would present different characteristics in the post-pandemic era.
6. Conclusions and Policy Implications
This study provided a novel framework for a comprehensive understanding of the spatial effect of public service accessibility on housing prices. Following this framework, we revealed the heterogeneous capitalisation effect of public service accessibility on housing prices, which provides policy implications for the rational allocation of public services and housing equity. The urban centre of Wuhan, China, was selected as a representative case study. The main conclusions obtained were as follows.
Spatial heterogeneity in the accessibility of public services was evident in the high-value units found in built-up areas, while low-value units were located at the urban fringe. Larger public services such as restaurants and bars, hospitals, primary and middle schools and banks had more significant clustering effects than smaller public services, such as convenience stores, clinics, kindergartens and ATMs. Various types of public services, such as recreational, medical, educational and financial facilities, all had capitalisation effects on housing prices. The explanatory power of the driving factors on housing prices obtained from the spatial association detector model was greater than that of the geographical detector model. Based on the spatial association detector model, the main driving factors affecting housing prices were distance to the nearest industrial land and distance to the nearest metro station, followed by accessibility to restaurants and bars and accessibility to ATMs. The interaction of any two driving factors strengthened the impact of each on housing prices in this study. We found that among the bivariate enhanced interactions for driving factors, the interaction between D_METRO and Ozone was the strongest. Among the nonlinear enhanced interactions for driving factors, the interaction between D_METRO and FLOOR was the strongest. The lessons learned from this study should be insightful for urban planning.
Several policy and practice recommendations could be drawn for housing equity. Firstly, the government, as an administrative subject, could assume responsibility for dealing with public affairs and developing policies and practices to provide adequate support and assistance for housing equity and equitable access to public services for urban residents. On the one hand, the taxation system reform could be accelerated. It is essential to assess property values in a timely and appropriate manner based on the availability of public services. On the other hand, the government could increase investment in basic public services such as leisure, education, healthcare and finance, and ensure equity in housing resources through income redistribution. For the urban fringe, establishing accurate management mechanisms for public service facilities and anticipating the actual needs of residents are important in order to develop sustainable public service policies. Secondly, as there are differences in the impact of various types of public service facilities on housing prices, policymakers or planners are recommended to consider the priority of public services when making spatial allocations of resources. Priority should be given to public services that are urgently needed in residents’ lives to reduce the inequities caused by capitalisation. It is proposed to improve the accessibility of the metro station by providing additional transport links to the metro. The subsequent provision of additional restaurants and ATMs to meet the basic needs of residents would reduce the disparity in property values due to the insufficient supply of public services. It is also recommended that policies be formulated to prioritise the reduction of environmental pollution around industrial areas, such as strict control of PM2.5 and ozone pollution caused by industrial production. On this basis, the layout of parks, green spaces and rivers/lakes will be improved, and the differentiation of living spaces will be alleviated through land exchange and urban redevelopment. Lastly, it is necessary to consider the combined effect of public services and transport and environmental factors. Our findings revealed a strong synergistic capitalisation between housing prices and locational, housing and environmental variables. Urban planners are therefore recommended to consider the impact of various factors on housing prices when formulating policies. Specifically, public services such as stores and clinics should be added in areas of low accessibility to reduce spatial segregation and social exclusion generated by negative externalities.