1. Introduction
Public services refer to the basic products and services related to the survival and development of residents, such as compulsory education, basic medical care, public infrastructure, and environment sanitation [
1]. These services not only meet the characteristics of publicity but also require the participation of the government. Several studies have found that urban public services have significant externalities, which can increase the satisfaction and happiness of residents, attract the flow of urban population, and gradually become an important factor in determining residents’ quality of life [
2,
3,
4]. Therefore, the distribution pattern of public service facilities promotes the formation and evolution of urban housing prices by influencing residents’ house purchase intentions. At the macro level, this distribution is not only a market response to the unbalanced allocation of urban space resources but also an important mechanism for promoting the separation of social space [
5]. Moreover, the level of public services in different cities is also different. The mismatch of housing supply and demand has resulted in soaring housing prices in advantageous cities. The premium effect of public service levels in the housing market further promotes competition among different social groups and exacerbates spatial inequality in China’s housing market. Achieving equalization of public service levels plays a very important role in coordinating the coordinated development of urban and rural areas and regions.
Existing studies have also explored the relationship between various public service levels and urban housing prices, but there is no consensus on the key factors affecting housing price differences in different regions. These works are mainly based on traditional characteristic price models, spatial regression models, and geographically weighted regression models. These models are limited to analyzing the significance and direction of the effect of certain types of facilities on housing prices. However, there are many factors affecting the development of China’s real estate market, and the influencing parameters of real estate prices are not stable. As shown in
Figure 1, China’s public service level has a non-linear effect on urban real estate prices. At this time, the linear relationship between variables may not be able to reveal the law of real estate price fluctuations and their influencing factors. From a non-linear perspective, we can become richer and obtain more in-depth conclusions by discussing the volatility of public services on housing prices in Chinese cities.
Based on previous research on China’s public service level [
6,
7,
8], we identified four types of public service facilities, namely education, medical care, environmental, and transportation services, to represent the level of public service in China. By using threshold regression models and by analyzing the difference between public service levels and residential prices in 30 large and medium-sized cities in China, we further explore the dynamic influence path of different regional public service levels on real estate price changes.
This study fills the following gap in the literature. First, we employ the threshold regression model in order to reveal the dynamic effect of China’s public service levels on housing prices based on the difference in housing prices among cities in resource allocation. Second, considering the heterogeneity of urban public service levels, we study the effect of public services on real estate prices by region and analyze the reasons thereof. Third, the literature generally uses official statistics of population urbanization rate indicators, which have certain drawbacks. The urbanization indicators obtained by processing night light data in this study have higher credibility.
The remainder of this paper is structured as follows:
Section 2 reviews the body of academic research on public services and the related literature on the effects of public services on housing prices.
Section 3 elaborates on the empirical methods employed and the data sources.
Section 4 shows the empirical results.
Section 5 discusses the results. Finally, the conclusions and related policy recommendations are presented in
Section 6.
2. Literature Review
In 1912, the French public jurist Leon Duguit conceived the concept of public service: “An activity is a public service, as long as it has the characteristics that it cannot be guaranteed unless it is through government intervention” [
9]. Eleanor Ostrom proposed the concept of “public pond resources” and discussed the possibility of autonomous management of “public ponds” [
10], which further enriched public service theory. Further studies on public services have also contributed to envisioning a perfect theoretical system of public service and guided subsequent research on the topic [
11,
12,
13].
American economist Tiebout [
14], for example, put forward the theory of “voting with feet” in the field of public service products in 1956. Among them, the research on the spillover effects of public services has really begun to attract scholarly attention. This theory mainly studies consumers’ preference in the selection of public service products and the efficiency of public service products, which allows one to expand and empirically analyze the problem from multiple angles. In this regard, the focus is mainly on public service facilities that are closely related to residents’ lives, such as the environment, education, transportation, and medical care.
In terms of the environment, Zhang et al. [
15] claimed that air pollution has a spillover effect on local housing prices, while Dai et al. [
16] noted that areas with higher environmental risks, such as gas stations and chemical companies, have lower housing prices, and different risks result in an inverted U-shaped relationship between housing prices and total environmental risks. Chen and Li [
17] confirmed that cities close to polluted rivers significantly reduce apartment prices, and buyers are willing to pay an additional premium for apartments far away from the two polluted rivers.
In terms of educational facilities, Han et al. [
18] found that the average educational premium of high-quality primary schools to housing prices is about 11%, and it is increasing every year. Wen et al. [
19] showed that the effect of educational facilities on housing prices is quite different; primary and secondary schools have significantly higher housing prices, while kindergartens are only valued by buyers of low and high housing prices. Wen et al. [
20] believed that educational facilities have a positive capitalization effect on housing prices, and elementary and junior high schools have significant school district effects.
In terms of transportation, Yang et al. [
21] noted that the premium and negative effects of public transportation on housing prices exist at the same time and they are spatially heterogeneous. Buyers of high housing prices are willing to pay more for staying away from public transportation. Tsai [
22] holds that improving transportation infrastructure can shorten the invisible distance among cities, promote interaction among cities, and promote the convergence of housing prices. Dai et al. [
23] showed that both the transfer station and non-transfer station of Beijing rail transit have a value-added effect on the price of surrounding houses, but the increase with respect to the transfer station is even greater.
In terms of medical care, Wang et al.’s [
24] research on several megacities in China finds that hospitals near communities are positively correlated with housing values; the farther away from the hospital, the lower the value of the house [
25].
While public services benefit urban housing prices, this relationship is not certain in all countries around the world. For example, ordinary housing has a significantly positive effect on the prices of transportation hubs, central business district, medical service center, and school district, while expensive houses do not [
26]. Andersson et al. [
27] estimated the implied price of Tainan’s urban high-speed rail and found that its effect on housing prices is marginal and even negligible. Pang and Jiao [
28] found that the effect of Metro Line 3 on property prices around Beijing is also negligible. Wen et al. [
29] showed that public transportation in Hangzhou City cannot increase the value-added effect of houses. Wen et al. [
20] and Wen and Tao [
30] also confirmed that the accessibility of buses harms the real estate value of Hangzhou. In addition, the influence of the degree of public services or infrastructure in many developed regions on housing prices is not so significant. For example, proximity to bus stops, train stations, or highways has no significant impact on the value of housing in rural areas in Northeastern Slovenia. Moreover, the proximity of railway tracks is significantly negatively correlated with price [
31]. The sizes of the houses and the distance from the city center have a greater impact on housing prices in Seoul, South Korea, than its transportation convenience [
32]. The negative impact of the transportation system on housing prices in Utah is even greater than the positive impact [
33]. The location and neighborhood characteristics of the house will not have a significant impact on the price of the house [
34].
Therefore, the conclusion that the degree of public services has a positive influence on urban housing prices cannot be applied to all regions. As Epple et al. [
35] stated, the Tiebout model only confirms that differences in public services between regions will cause relative housing prices to rise, but it cannot explain the specific relationship between public services and the overall housing price level. Thus, some strict assumptions in Tiebout’s theory may not be completely correct, but research does confirm a certain Tiebout effect in China, that is, the supply of local public goods has a certain effect on housing prices.
In short, we can see that the externalities of China’s public service level can produce positive, marginal (insignificant), or negative externalities toward neighboring real estate. There is still no unified conclusion. There are still a few studies on the dynamic effect of public service levels on housing prices. To make up for these research gaps, it is noteworthy to study the influence of China’s public service level on urban housing prices.
6. Conclusions
This study uses a panel threshold model to explore the mechanism of dynamic effects of public services and housing prices at the city level in China. The results of the study show that public services and urban housing prices are heterogeneous at different income levels and urbanization development stages. Regarding the per capita disposable income of urban residents, the effect of public services on urban housing prices shows an inverted U-shaped trend. When the income level is less than RMB 31,719.92, the urban housing prices increase along with the level of public services. When the income level is within the range of RMB 31,719.92 to RMB 40,974.29, the urban housing prices will decrease as the level of public services rises. When it exceeds RMB 40,974.29, this negative driving effect is enhanced. Regarding the level of urbanization, the driving effect of the level of public services on urban housing prices increases with the increase in urbanization. When the urbanization level exceeds its threshold of 1.1633, its positive driving effect is greater. From a regional perspective, the level of public services positively contributes to the housing prices of eastern, western, and northeastern cities, and the promotion effect of eastern cities is significantly higher than that of western and northeastern cities.
Based on this research, we can better explain the dynamic effects of public service levels on urban housing prices and provide new insights for policymakers regarding the regulation of the housing market. We believe that local governments should focus on the quality rather than the quantity of public services provided. China’s real estate market is highly differentiated, and its development is not only affected by economic and political factors but also by demographic, cultural, and other related factors. A series of macro-control policies and measures promulgated by the state will play a key role thereof. Public services such as health care, environment, convenience facilities, and education are most closely related to residents’ daily lives. The effect of public service capitalization on housing prices differs in spatial agglomeration and the distribution of influencing factors. This requires local governments to prioritize the quality of regional public service provision. Improving public services will increase housing demand in underdeveloped cities, and upgrading education facilities and services in developed cities will increase housing demand.