1. Introduction
As a pillar industry of the national economy, the real estate industry occupies an important position in terms of promoting regional economic growth and boosting domestic demand and urbanization in China. Housing investment is a core component of the healthy development of the real estate industry. Although the industry is currently facing uncertainties in terms of the external environment and policies, housing investment is characterized by a resilient evolution of alternating expansion and contraction. During the economic upturn, to promote the stable and healthy development of the real estate market, the government successively put forward the following policies: “houses are for living, not for speculation”; “stable land prices, stable house prices and stable expectations”; “real estate should not be used as a means of stimulating the economy in the short term”; and “solving the outstanding housing problems in big cities”. During the economic downturn, especially after the financial crisis in 2008, real estate housing investment remained at around 20% of fixed asset investment and 13% of GDP. Real estate housing investment grew at an average annual rate of 9.1% from 2011 to 2019, higher than the average annual GDP growth rate. It is evident that housing investment is an important driving force for the economic repair of cities, showing strong resilience. Accordingly, how can urban housing investment resilience be measured scientifically? What are the factors affecting housing investment resilience? Answering these questions will provide theoretical references to help strengthen urban housing investment resilience in China moving forward.
Resilience, as a concept in physics, refers to the process of recovery and growth of an object under the impact of external forces [
1]. With the development of resilience theory has seen the concept of resilience introduced in various disciplines, such as urban planning, ecology, and management, with researchers conducting theoretical explorations [
2]. In 2007, the Resilience Alliance proposed the concept of urban resilience, specifically including urban facilities and the urban environment, metabolic flows, governance networks, and social dimensions [
3]. At present, research on urban resilience focuses mostly on issues such as socioeconomic resilience, spatial planning, urban clusters, and urban governance [
4,
5,
6], and panel models, system coupling, comprehensive evaluation, network analysis, and other methods are used to perform conceptual analyses of urban resilience, spatiotemporal evolution, influencing factors, and formation mechanisms [
7,
8,
9]. Most studies have focused on the developmental characteristics of regional urban resilience and the evolution of its industrial structure. Whereas accurately measuring industrial resilience and characterizing its evolutionary development pattern and influence mechanism are hot topics, there have been few studies on the measurement of resilience and the spatiotemporal evolution of urban housing investment; research on the formation mechanisms of housing investment resilience according to city type is also lacking. Scholars have mainly studied housing investment in terms of the investment environment and differences in investment types [
10,
11] and have argued that the level of housing investment is influenced by the economic growth rate, housing stock, total social investment, population size, and housing policies [
12,
13]. Research on housing investment in China can be broadly categorized into two types of influencing factors: housing supply (economic scale, land supply, ecological environment, etc.) and market demand (spatial game, ability to pay, market expectations, etc.) [
14,
15]. The choice of research objects occurs at the micro, meso, and macro levels, and the temporal characteristics and spatial aggregation evolution patterns of housing investment in major cities are explored with the help of spatiotemporal evolutionary analysis [
16]. In addition, some scholars have analyzed cross-regional choices in real estate enterprise investment and the impact of population mobility on residents’ housing investment, starting with the housing investment behavior of enterprises and residents [
17,
18]. In general, there is a foundation of research on urban housing investment and its influencing factors but a lack of perspectives on resilience in the context of the cyclical pattern of housing investment. Furthermore, most studies have analyzed the net effect of various factors on housing investment as a whole from a regional perspective. Urban housing investment is closely linked to regional socioeconomic development, but few studies have considered identifying different city classes and further exploring the spatiotemporal differences in the development of housing investment in different types of cities, as well as their formation mechanisms.
In this study, the housing investment resilience of 35 large and medium-sized cities in China is measured using an economic resilience approach based on the three-factor theory of investment decisions and the requirements of resilient city development. The fuzzy set qualitative comparative analysis method is used to analyze the differences in housing investment resilience between first-tier and non-first-tier cities, and similarities and differences in the factors driving housing investment resilience in the two types of cities are further explored. On the basis of theoretical analysis, model construction, and empirical testing, this study comprehensively reveals the spatiotemporal evolution of urban housing investment resilience and the mechanism of its formation, providing a reference for the promotion high resilience and quality development in urban housing investment in China moving forward.
2. Research Methodology and Data Processing
2.1. Research Methodology
2.1.1. Housing Investment Resilience Measure
(1) Housing investment growth characteristics
In 2011, the “Eight New National Policy Measures” specified an increase in the down payment ratio for home purchases, and in 2013, the “Five New National Policy Measures” proposed strict restrictions on purchases. Furthermore, the growth rate of real estate investment in housing declined significantly between 2011 and 2013, indicating a shift in China’s housing investment from “overheated” to “slightly overheated” under the influence of national policy regulation. Subsequently, the PMI, an indicator of economic prosperity, was below the Ronggu line for six consecutive months in 2015, and China’s economy entered “three overlapping periods” (a period of shifting growth rates, a period of painful structural adjustment, and a period of digestion of economic stimulus). The growth rate of real estate housing investment remained in decline during 2014–2015, showing the characteristics of a cold phase. In 2015, the real estate industry entered a period of supply-side structural adjustment in order to dissolve real estate inventories and promote the healthy development of the industry, which helped the market heat up again. The 2016 Central Economic Conference proposed that “houses are for living, not for speculation”, and although real estate policy was still in a period of tightening, the growth rate of investment in real estate housing showed a steady rebound from 2016 to 2019 (
Figure 1). In short, although the growth rate of real estate investment declined rapidly, it was able to rebound steadily under relevant policies and industrial structural adjustments, showing an overall trend of rebound after a decline, revealing the distinctive resilience of real estate.
(2) Housing investment resilience indicator
In this study, we draw on Martin’s measure of economic resilience [
19] to construct a housing investment resilience indicator. Using the magnitude of fluctuations in the amount of housing investment at the time of the shock as a reference point, urban housing investment resilience is measured as the ratio of the change in actual and counterfactual investment fluctuations, representing the ratio of relative impediment to recovery. Counterfactual data refer to the difference in each city between the change in housing investment and the expected change, providing feedback on the tendency for housing investment to decline or rebound. This method not only enables an examination of the resilience of the urban housing investment system in the event of a shock but also provides a useful measure of housing investment resilience. It assumes that the volatility of housing investment resilience in a given city during a recession will follow the national rate of contraction after a shock and the national rate of expansion during a recovery period. In conjunction with the cumulative growth rate of housing investment in China discussed in the previous section, the ratio of actual and counterfactual changes in housing investment volatility will be scaled according to two cycles, 2011–2013 and 2014–2019, as calculated by the following formula:
where
is the housing investment resilience index for city
i in year
t;
and
are the amount of property development investment in city
i in years
t and
t−
k, respectively; and
and
are the sums of property development investment in cities in years
t and
t−
k, respectively.
2.1.2. Qualitative Comparative Analysis Methods
The process of shaping the resilience of urban housing investments is complex and often difficult to explain with a single factor. Qualitative comparative analysis (QCA) can identify how a number of factors affect the final outcome in combination. Accordingly, in this study, we use the QCA approach to analyze the mechanisms that shape the resilience of urban housing investment. The QCA approach is based on the idea of set theory and Boolean algebra operations, combining the advantages of qualitative and quantitative analysis to investigate the ideal set of paths leading to the final outcome from a combination of multiple antecedent variables [
20]. The method takes a holistic view of the relationship between factor configurations and outcome variables, emphasizing the complexity of causal relationships and the existence of multiple pathways that can produce the same outcome [
21]. Consistency and coverage are calculated, and the antecedent configurations that have the greatest influence on the outcome variable are selected. The formula is as follows:
where
X is the set of all antecedent variables,
Y is the set of outcome variables,
xi is the individual antecedent condition, and
yi is the outcome variable corresponding to
xi. Consistency is a sufficient condition for determining whether
X is a sufficient condition for
Y. A sufficient condition is considered to hold if it is higher than 0.75; if the sufficiency of a single variable (
xi) is greater than 0.9, then
xi is a necessary condition for
Y [
22]. Coverage describes the strength of
X’s explanation of
Y. The greater the coverage, the stronger the explanation of the outcome variable (
Y) by the histogram path (
X).
2.2. Theoretical Framework Construction and Analysis of Influencing Factors
Housing investment resilience refers to the fact that against the backdrop of an economic downturn, real estate investment has maintained a high level of development, lending strong support to the smooth operation of the macro economy [
23,
24]. Specifically, housing investment resilience, as one of the manifestations of the regional economy, is an organic whole. After a shock, housing investment goes through a process of "slightly cold", "too cold", "slightly hot", and "too hot". To achieve the transition from decline to rebound, the housing industry needs to consolidate resources, restructure, and improve its ability to adapt to the external environment so as to maintain a stable level of housing investment. This process has both the common characteristics of urban resilience and the individual attributes of housing investment.
With respect to urban resilience, urban economic resilience is expressed in terms of economic diversity, employment levels, and economic stability in the event of risk. Urban social resilience is the reserve status and supply capacity of social resources, which determine a system’s ability to withstand the challenges of shocks. Urban institutional resilience is the ability of government institutions to govern; in particular, it is the ability of government to exercise organization, management, planning, and action following external risk shocks. Urban ecological resilience is the ability of urban ecosystems to recover from shocks, such as environmental pollution, ecosystem overload, and sharp reductions in public green space. Urban infrastructure resilience is a system’s ability to cope with and recover in the face of risky perturbations when population density increases, for example, the ability to secure facilities and lifelines, such as transport, water supply, electricity supply, and healthcare. To this end, the factors affecting urban resilience systems are integrated and combined with the reality of housing investment development. In this paper, we considers economic growth, infrastructure, policy support, and the labor market as important factors regulating the resilience of housing investment.
With respect to housing investment, houses, as commodities, are necessarily influenced by many factors. Revenue, cost, and expectations are important internal influences on housing investment. This is in line with China’s policy objective of "stable land prices, stable house prices, and stable expectations". The level of income is closely linked to the economic development of cities. When the economy experiences a downturn, investments, consumption, and savings among stakeholders, such as the government, enterprises, banks, and residents, are all affected, leading to impacts on investment resilience. Land costs are the primary cost involved in housing development, and the impact of shrinking land supply leads to higher housing development costs, which is not conducive to housing investment resilience. House price expectations provide feedback on the potential of housing market demand. When house price expectations suffer a negative shock, investors’ decisions and market expectations are seriously affected, ultimately affecting the development of housing investment resilience.
In conclusion, based on the synthesis of the above analysis, the three elements of investment and the urban resilience system work together in the development of housing investment resilience to form the conceptual connotation of housing investment resilience investigated in this paper. Housing investment resilience is characterized by complexity, openness, and comprehensiveness, including not only the supply and demand dimensions of urban development, such as the economy, policy, and labor but also sustainability investment dimensions, such as returns, land, and expectations. The three elements of investment are the internal factors affecting housing investment, whereas the urban resilience system comprises the external factors affecting housing investment, which constitute the internal generative logic and external driving mechanism of housing investment resilience development (
Figure 2).
2.3. Data Processing
2.3.1. Data Sources and Scoping Study
The outcome variable in this paper is urban housing investment resilience. We use the commodity residential investment resilience index for 35 large and medium-sized cities as a proxy variable [
25]. The three elements of investment—returns, costs, and expectations—are proxied by the average growth rate of house prices in the previous three years [
26], the growth rate of land acquisition costs [
27], and population density [
28], respectively. As specified in the research of Hu [
27], an increase in land acquisition costs increases house prices, and an increase in house prices promotes real estate investment. Thus, all three elements of investment decisions are positively related to housing investment resilience. Economic growth indicators are represented by the GDP growth rate [
25], and infrastructure construction investment is represented by the growth rate of local general public budget expenditures [
29]. Labor market indicators are expressed in terms of employment rates [
30]. The policy factors are real estate policy and monetary policy [
31]. The proxy variable for real estate policy is the weighted average lending rate for individual housing [
32]. To capture the differences in real estate policies among the 35 large and medium-sized cities, the CPI of each city (with 2011 as the base period) was used to convert them into real interest rates. Monetary policy is defined as loose monetary policy according to the Monetary policy Implementation Report issued by the People’s Bank of China. If the report explicitly mentions “easing”, then the monetary policy is directly defined as loose. If there is no explicit statement, then it is classified as loose monetary policy if interest rates are reduced and as tight monetary policy if the opposite is true according to the actual operation of the year [
33]. The years 2012, 2014, 2015, 2016, 2018, and 2019 were assigned a value of 1 for accommodative monetary policy, and the remaining years were assigned a value of 0. An increase in personal housing loans leads to an increase in house prices, which further leads to an increase in housing investment according to Hu [
27]. Thus, there is a positive relationship between economic growth, the growth rate of local general public budget expenditures, employment rates, the weighted average lending rate for personal housing, accommodative monetary policy, and housing investment resilience.
In this study, we selected 35 large and medium-sized cities in China for urban housing investment resilience research. The research data are sourced mainly from the CEIC macroeconomic database and the National Bureau of Statistics, with supplementary data from the China City Statistical Yearbook and the China Real Estate Statistical Yearbook. According to the 2019 City Business Attractiveness Ranking, 35 large and medium-sized cities are divided into first-tier and non-first-tier cities based on the results for the 337 Chinese cities above the prefecture level. There are 17 first-tier cities, including 4 super-first-tier cities, namely Beijing, Shanghai, Guangzhou, and Shenzhen, and 13 new first-tier cities, namely Chengdu, Hangzhou, Chongqing, Wuhan, Xi’an, Tianjin, Nanjing, Changsha, Zhengzhou, Qingdao, Shenyang, Ningbo, and Kunming. The remaining 18 cities, namely Shijiazhuang, Taiyuan, Hohhot, Dalian, Changchun, Harbin, Hefei, Fuzhou, Xiamen, Nanchang, Jinan, Nanning, Haikou, Guiyang, Lanzhou, Xining, Yinchuan, and Urumqi, are all non-first-tier cities.
2.3.2. Measurement and Calibration
QCA methods based on set theory aim to identify sufficient or necessary subset relationships between the configurations of different antecedent variables and outcome variables. The QCA method is divided into a crisp set, a multi-value set, and a fuzzy set. The crisp set is mainly used to analyze binary variables, the multi-value set is mainly used to analyze multivariate discrete variables, and the fuzzy set is used to analyze continuous variables between 0 and 1. Because all variables investigated in this paper are continuous variables, except for monetary policy, which is a binary variable, it is appropriate to use the fsQCA method to study them. Therefore, performing fsQCA analysis, the individual antecedent variables are calibrated and transformed into an ensemble concept; that is, all raw data are converted into fuzzy affiliation scores within the range [0, 1]. In this paper, to calibrate the raw data into a fuzzy set, three anchor points need to be identified, namely fully affiliated, intersection, and fully unaffiliated. The intersection point is the intermediate point that distinguishes between fully affiliated and fully unaffiliated and indicates whether the case belongs to the maximum fuzziness point of a set. In this paper, the three-valued fuzzy set calibration method of Du et al. (2020) is adopted, with the variables using 75%, 50%, and 25% quantile values as the thresholds for completely affiliated, crossover point, and completely unaffiliated, respectively [
34]. In this paper, to eliminate the effect of the time factor, the price type indicators are treated as constant prices, with 2011 as the base period; the calibration data of each variable are shown in
Table 1.
5. Conclusions and Discussions
Based on the theory of the three elements of investment decisions and urban resilience theory, a research framework of urban housing investment resilience and its influencing factors was constructed. With 35 large and medium-sized cities in China as the research objects, in this study, we investigated the spatiotemporal evolution characteristics, multiple influencing factors, and driving paths of housing investment resilience in first-tier and non-first-tier cities using spatiotemporal analysis and qualitative comparison methods. The findings are as follows.
First, the overall resilience value of urban housing investment during the observation period was low and can be divided into three development stages, showing an M-shaped fluctuating evolution with stage and cyclical developmental characteristics and a clear gap between non-first-tier and first-tier cities. Spatially, there are distinct polarization differences and imbalances. The gradient effect between first-tier cities is gradually weakening, whereas non-first-tier cities are characterized by the Matthew effect, with the strongest cities becoming stronger.
Second, the three internal decision conditions of returns, costs, and expectations cannot, by themselves, constitute the necessary conditions for high levels of resilience in urban housing investment decisions. There are three paths to high levels of housing investment resilience in first-tier cities, namely "return- and cost- driven", "cost-driven", and "expectation-driven". There are also three paths to high levels of housing investment resilience in non-first-tier cities, namely "return- and cost-driven", "return- and expectation-driven", and "expectation-driven".
Third, the resilience paths to high levels of housing investment in first-tier cities and non- first-tier cities are the same, in addition to different dedicated paths for each city type. The same path is "return- and cost-driven". Whereas both types of cities are "expectation-driven”, the external support conditions differ considerably.
Finally, there are potential substitution relationships between all three paths in first-tier cities and non-first-tier cities. Overall, real estate policy is an important condition for housing investment resilience in first-tier cities, and infrastructure development and labor markets are important conditions for housing investment resilience in non-first-tier cities.
Most studies have focused on regional housing investment growth, expansion trends, spatiotemporal characteristics, influencing factors, and their impact, but there has been a lack of in-depth integration with the cyclical patterns of housing investment ups and downs. Few studies have investigated housing investment resilience, and few have examined the impact mechanisms for different types of cities, making it difficult to illuminate the “black box” of urban housing investment. On the basis of previous research, in the present study, we combined the three elements of investment decisions with the theory of urban resilience systems, expanding the seven conditions affecting urban housing investment and constructing a theoretical research framework for urban housing investment resilience. On the other hand, this study extends the comparative study of groupings in explaining complex causal relationships through the “configuration perspective”, analyzing the reasons for differences in the driving paths of housing investment resilience between first-tier and non-first-tier cities to compensate for the shortcomings in the explanation of the influencing factors of housing investment under the weighting perspective. Analysis of the linkage effects of multiple factors reveals the complex causal relationships behind urban housing investment.
The findings reported in this paper have the following policy implications. First, the evolution of urban housing investment resilience in the face of external shocks is cyclical in nature, going through a process of "slightly cold", "too cold", "slightly hot", and "too hot". Following the evolution of urban housing investment resilience requires advanced planning in the face of the unknown risks of external shocks. As demonstrated, good urban housing investment resilience is not just about being resilient in the face of shocks. It should be reflected in the continuous optimization of the internal structure and order of the housing investment resilience system before external shocks and the continuous improvement of the stability and resilience of the resilience system. Ultimately, this effectively promotes the sustainable and healthy development of urban housing investment markets.
Second, costs and returns are important factors influencing the development of urban housing investment resilience. Combined with the cyclical evolution pattern of housing investment resilience, costs and returns become the direction of early intervention and focused regulation within the investment resilience system. The relationship between the costs and returns of urban housing investment needs to be integrated and balanced with a focus on the linkage effect between the two. That is, there is not always a positive correlation between costs and returns, and a competing relationship between the two is always present. It is important to avoid an imbalance in the development of the costs of housing openness and the return on investment. To this end, a focus on adjusting the cost–benefit structure of the housing investment market is necessary to effectively enhance the resilience of urban housing investment.
Third, there are significant differences in the evolution of housing investment resilience in different types of cities. There are also differences in terms of the focus of attention on strengthening the resilience of urban housing investment in the future. Specifically, for high-grade cities with better development infrastructure, real estate policy is the core condition affecting the development of their resilience systems. This category of cities needs to focus on housing policy regulation to promote the stable and healthy development of land prices, house prices, and expectations through localized and multipronged approaches. However, for low-grade cities with poor development infrastructure, infrastructure development and labor markets are the core conditions for the development of resilient systems. Therefore, infrastructure development and an adequate labor job market are also important conditions for attracting capital investment.