# Housing Rental Incentive and Development Empirical Analysis from the Perspective of Financial Decentralization

Authors: Xuanxuan Zhang
Journal: Discrete Dynamics in Nature and Society
 (2021)

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## Abstract

This study aims to evaluate and analyze the implementation effect by central government of the housing rental market in different cities in the context of rent and purchase in parallel. Game theory and panel data empirical analysis were used to study the subsidy transmission mechanism of the development of the housing rental market under the perspective of financial decentralization. The central finance played an indirect role in the development of the local housing rental market, and the local government’s support for the local housing rental market was an intermediary variable. To promote the rapid and healthy development of the domestic housing rental market, the central government needs to make top-level design and give certain local policies and financial support. At the same time, local governments must actively implement relevant policies of the central government and support corresponding local support policies.


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## Body

## 1. Introduction

The current economic and financial situation was becoming increasingly complex. How to play the important supporting role of real estate to the economy was very important [1]. The cultivation and development of the housing rental market are expected to effectively optimize the structure of the real estate market on the supply side, balance the stock and increment, calm the risks of the real estate market and industrial chain, more actively meet the needs of people’s livelihood on the demand side, and promote the sustainable and healthy development of the real estate market [2, 3].The CPC Central Committee and the State Council attach great importance to “Houses are used to live in, not to be used for speculation.” In recent years, housing rental related policies have been introduced to actively support the development of the housing rental market since December 2016. In general, the main policies and measures related to housing rental in China could be summarized into the following categories. Firstly, strengthen financial support by giving tax incentives and increase financial support, improving housing rental land supply; secondly, improved financial support by promoting housing rental innovative financial products, allowing housing accumulation fund to pay rent; thirdly, strengthened market supervision by clearing the same rights, building housing rental service supervision platform, improving enterprise and personal supervision and management of housing rental enterprises.After China implemented the tax reform in 1994, the focus of financial power shifted upward, and the focus of administrative power was suppressed [4]. The main problem caused was that for the rigid expenditure of general public services, local governments need to operate through debt [5]. The development of the housing rental market also has the dual attributes of the economy and public welfare and the different places have different economic situations, industrial foundations, population structures [6]. So the basis, conditions, and motivation for the development of housing rental were different in each region. For example, first-tier cities such as Beijing, Shanghai, and Guangzhou could attract more nonlocal people, while high housing prices make most of the urbanization inflow population only have to choose rental housing. In addition, the integration strategy of industry and city implemented by local governments has built part of the rental housing planned by local governments to solve the housing needs of the inflow population for industrial development in recent years. At the same time, the implementation willingness and intensity and progress of different regions were different after the central government issued policies to guide the development of housing rental. Therefore, it is necessary to study the development of the local housing rental market under the dual background of rent and purchase in parallel and financial decentralization between central and local through the methods of gaming and empirical analysis.


## 2. Review of Domestic and Foreign Studies

Foreign countries started early and the market was relatively mature for the development of housing rental. Due to the emphasis of traditional welfare states on the state and government for social responsibility, housing was an important welfare content, and welfare expenditure was limited, based on the “housing-welfare replacement theory” proposed by Kemeny [7]; that is, the welfare level and housing self-ownership rate in Europe and the United States were negatively correlated. Housing subsidies have a certain crowding-out effect on the welfare of traditional residents. Different residents have different tendencies towards welfare, and they also have different choices for buying or rental housing. Davies et al. [8] analyzed that different choice between rental and purchase makes different happiness. In recent years, with the increasingly complex factors of China’s economic and financial situation and avoiding the kidnapping of economic growth by real estate, China has introduced a series of policies and measures, such as “Houses are used to live in, not to be used for speculation,” to promote urbanization development through developing the housing rental market and suppressing the risks of the real estate market. From a demand-side perspective, Li et al. [9] empirically analyzed the regional differences and influencing factors of rental housing development in many cities above the prefectural level in China and found that the development and changes of rental housing in China from 2000–2010 were mainly related to the permanent resident population, migrant population ratio, per capita housing area, and other factors. From the perspective of the supply side, on the capital side, because the development of housing rental was still in the early stage and the investment return period was so long, some scholars analyzed from the perspective of market-oriented capital source and income balance. Liu and Liu [10] put forward the real estate securitization, development investment real estate trust fund, issuing housing construction public debt measures to solve the financial dilemma facing housing rental construction from western developed countries public housing construction experience, innovative low-rent housing financing mode. Yuan et al. [11] analyzed the risk allocation principles and allocation strategies of the PPP model, then explored the various possible risks in the housing rental PPP projects, and made rationalization suggestions for the risk allocation scheme. On the land side, Lin et al. [12] took Beijing as the sample. Through analyzing the background motivation of collective land construction of rental housing, they found that improving the transfer mechanism of collective land into the market and the construction of supporting facilities were of certain supporting significance to the development of the housing rental market. Relying purely on social capital and other market forces was not conducive to the formation of large-scale development, and the progress was so slow based on the important role of the government in China’s economic and social development, and the housing rental itself has a certain public welfare property. Therefore, in addition to introducing corresponding policies for guidance, the government has also introduced some corresponding financial subsidy measures to support the development of housing rental (The central government subsidy pilot began in 2019, and 24 cities have been shortlisted).Since China has implemented the tax reform since 1994, local governments “turn over the financial power and assume the administrative power” [13]. They took on major tasks such as local infrastructure construction and often solved them through debt and other means [14]. However, the development and investment term of housing rental was so long, which led to the increasing pressure of capital expenditure, and the willingness and intensity for the development of the housing rental market were different. Some cities actively support the development of the housing rental market by increasing land supply, special financial subsidies (Shanghai, Xiamen, Chengdu, and other cities have also issued the corresponding “housing rental special fund subsidy operation rules”), and interest expenditure subsidies for local financial institutions (For example, Fujian actively carried out housing rental financial innovation, and actively supported the development of housing rental market through housing rental income pledge business, asset securitization products, interest expenditure subsidies, and special support fund for housing rental enterprises loans). Otherwise, the development process of some cities was so slow. So we could describe how local governments carry out central government’s policies, which was a lacking perspective of recent research on the development of the housing rental market under the background of financial decentralization.


## 3. Analysis of the Game Mechanism

### 3.1. Game Model Building

The central government was the top-level designer and original maker who deduced the development of housing rental. Its original intention and interest appeal were promoting the stable and healthy development of the real estate market, achieving its own place and maximizing the overall interests of the society. Local governments were the implementers of the central policy and the makers of local policies, and their interest demands were the balance of political, economic, and social interests. Therefore, we can consider the framework of the game model; the development of local housing rental markets needed to rely on the support of the central policies and funds and the execution of local governments. The central government would give certain financial subsidies to local governments according to the development situation and plan of housing rental. At the same time, local governments would also make scientific arrangements for the development of the housing rental market according to the basis of local economic and social development. There were two strategies for the two governments, subsidies and nonsubsidy, active implementation, and negative implementation.We setA as a housing rental subsidy from the central finance to the local finance—the specific path including direct subsidies and transfer payments, etc. E (L1) and E (L2) provided gains when local governments actively and negatively implement strategies. In order to ensure the effectiveness of the central subsidies, when the central after the local subsidies, would supervise the effect of its implementation, assuming the cost of supervision for C; if the local government made negative implementation, the central government would punish it, using specific measures including not to continue to subsidies, administrative punishment, capital punishment, etc. We use P1 as punishment cost. On the contrary, if the central government did not implement subsidies to local governments, it would not supervise it, and the cost of supervision is 0. No matter whether the central government subsidies local governments, they expected local governments would actively support the development of the housing rental market. Whether local governments implement it actively or negatively, they expected the central government would give certain subsidies. Therefore, it could be envisaged that the guiding cost of the local government to choose active or negative execution is P2. From another perspective, if the central government did not motivate the local government, then the local government would choose the strategy of negative execution: The costs that E has to pay when actively implementing housing leasing policies, such as land transfer fee concessions, tax exemptions, and other economic subsidies for enterprises actively participating in housing leasing. According to the above assumptions, the return matrix of both parties under this game framework can be summarized as in Table 1.Table 1
Game matrix between central and local governments.
Local governmentsActive execution (q)Negative execution (1 −q)Central governmentsSubsidies (p)E (L1) − A − C, A − EE (L2) − A − C, A − P1No subsidies (1 −p)E (L1) − P2, −EE (L2) − P2, −P1According to Table1, for the central government, the size of A + C and P2 determines their strategy, not choosing subsidies when A + C > P2 and on the contrary, the central government choosing subsidies. For local governments, the size of E and P1 determined its strategy, choosing negative execution when E > P1 and vice versa choosing positive execution. The static Nash equilibrium is virtually absent due to the uncertainty. Therefore, the bilateral game process needs to be studied by means of evolutionary game analysis. The strategic choices made by both parties were rational and independent and repeated during multiple decision-making processes. The probability of central government adopting subsidy and nonsubsidy was p and 1 − p, and local governments taking positive implementation and negative implementation were q and 1 − q, respectively. At this time, the central government’s income matrix was as follows:(1)M=EL1−A−CEL2−A−CEL1−P2EL2−P2.Hybrid dynamic benefits wereEC=qEL1+1−qEL2+pP2−A−C. The probability of the calculated subsidies from the central government must be met p⌢=p1−qP2−A−C. The probability of local governments actively implementing their strategies must be met q⌢=1−qP1−E. Considering the strategy of central and local governments as a dynamic system, then there were four local equilibrium points between both sides, (0, 0), (0, 1), (1, 0), (1, 1), respectively. The stability of its equilibrium point was obtained from the local stability analysis of the Jacobian matrix obtained by the system, and the Jacobian matrix was as follows:(2)J=1−2pP2−A−C001−2pP1−E.According to the local stability analysis method of the Jacobian, the stability analysis of the above four equilibrium points is performed, as shown in Table2.Table 2
Local equilibrium point expression for the game process.
Equilibrium PointDeterminant ofJTraces ofJ(0, 0)P2 − A − CP1 − E(0, 1)P2 − A − CE − P1(1, 0)A + C − P2P1 − E(1, 1)A + C − P2E − P1


### 3.2. Discussion of Game Model

The local stability of each equilibrium point obtained in Table2 was discussed in 4 cases:(1)
WhenP2 > A − C, P > E, that is, both J determinant and traces of J were timing, (0, 0) was an unstable point. (0, 1) and (1, 0) were two saddle points. (1, 1) was a stability point. Evolutionary game equilibrium strategy was (Subsidy, Active Execution), p = 1, q = 1. For central governments, the cost of subsidies was less than benefits. For local governments, the proceeds of positive execution outweigh the cost. The equilibrium at this time belongs to the pure strategy Nash equilibrium, and the strategy of both sides chose according to their own situation, which has nothing to do with the other side. (2) When P2 > A − C, P < E, that is, the symbols of the J determinant and the J traces were positive and negative times, respectively. (0, 0) and (1, 1) were two saddle points. (0, 1) was an unstable point. (1, 0) was a stability point. Evolutionary game equilibrium strategy was (Subsidy, Negative Execution), p = 1, q = 0. For central governments, the cost of subsidies was less than benefits. For local governments, active execution costs were more than revenue. (3) When P2 < A − C, P > E, that is, the symbols of the J determinant and J traces are negative and timing, respectively. (0, 0) and (1, 1) were two saddle points. (1, 0) was an unstable point. (0, 1) was a stability point. Evolutionary game equilibrium strategy was (Not Subsidy, Actively Execution), p = 0, q = 1. For central governments, the cost of no subsidies was less than income. For local governments, active implementation costs were less than income. (4) When P2 < A − C, P < E, that is, when the symbols of the J determinant and the J traces were negative, (0, 1) and (1, 1) were two saddle points. (1, 1) was an unstable point. (0, 0) was a stability point. Evolutionary game equilibrium strategy was (No Subsidy, Negative Execution), p = 0, q = 0. For central governments, the cost of no subsidies was less than income. For local governments, active execution costs less than income. The replication dynamic phase diagram of both parties is shown in Figure 1.Figure 1
Dynamic phase diagram of the scenario evolution game process.According to the above analysis, to make the domestic housing rental market healthy under the financial decentralization system, we need to realizeP2 > A − C and P > E, that is, the central government subsidies of local government and local governments should actively implement the relevant central policies, supporting the corresponding local support policies, establishing the rent and buy housing system, letting all the people have houses to live in.


## 3.1. Game Model Building

The central government was the top-level designer and original maker who deduced the development of housing rental. Its original intention and interest appeal were promoting the stable and healthy development of the real estate market, achieving its own place and maximizing the overall interests of the society. Local governments were the implementers of the central policy and the makers of local policies, and their interest demands were the balance of political, economic, and social interests. Therefore, we can consider the framework of the game model; the development of local housing rental markets needed to rely on the support of the central policies and funds and the execution of local governments. The central government would give certain financial subsidies to local governments according to the development situation and plan of housing rental. At the same time, local governments would also make scientific arrangements for the development of the housing rental market according to the basis of local economic and social development. There were two strategies for the two governments, subsidies and nonsubsidy, active implementation, and negative implementation.We setA as a housing rental subsidy from the central finance to the local finance—the specific path including direct subsidies and transfer payments, etc. E (L1) and E (L2) provided gains when local governments actively and negatively implement strategies. In order to ensure the effectiveness of the central subsidies, when the central after the local subsidies, would supervise the effect of its implementation, assuming the cost of supervision for C; if the local government made negative implementation, the central government would punish it, using specific measures including not to continue to subsidies, administrative punishment, capital punishment, etc. We use P1 as punishment cost. On the contrary, if the central government did not implement subsidies to local governments, it would not supervise it, and the cost of supervision is 0. No matter whether the central government subsidies local governments, they expected local governments would actively support the development of the housing rental market. Whether local governments implement it actively or negatively, they expected the central government would give certain subsidies. Therefore, it could be envisaged that the guiding cost of the local government to choose active or negative execution is P2. From another perspective, if the central government did not motivate the local government, then the local government would choose the strategy of negative execution: The costs that E has to pay when actively implementing housing leasing policies, such as land transfer fee concessions, tax exemptions, and other economic subsidies for enterprises actively participating in housing leasing. According to the above assumptions, the return matrix of both parties under this game framework can be summarized as in Table 1.Table 1
Game matrix between central and local governments.
Local governmentsActive execution (q)Negative execution (1 −q)Central governmentsSubsidies (p)E (L1) − A − C, A − EE (L2) − A − C, A − P1No subsidies (1 −p)E (L1) − P2, −EE (L2) − P2, −P1According to Table1, for the central government, the size of A + C and P2 determines their strategy, not choosing subsidies when A + C > P2 and on the contrary, the central government choosing subsidies. For local governments, the size of E and P1 determined its strategy, choosing negative execution when E > P1 and vice versa choosing positive execution. The static Nash equilibrium is virtually absent due to the uncertainty. Therefore, the bilateral game process needs to be studied by means of evolutionary game analysis. The strategic choices made by both parties were rational and independent and repeated during multiple decision-making processes. The probability of central government adopting subsidy and nonsubsidy was p and 1 − p, and local governments taking positive implementation and negative implementation were q and 1 − q, respectively. At this time, the central government’s income matrix was as follows:(1)M=EL1−A−CEL2−A−CEL1−P2EL2−P2.Hybrid dynamic benefits wereEC=qEL1+1−qEL2+pP2−A−C. The probability of the calculated subsidies from the central government must be met p⌢=p1−qP2−A−C. The probability of local governments actively implementing their strategies must be met q⌢=1−qP1−E. Considering the strategy of central and local governments as a dynamic system, then there were four local equilibrium points between both sides, (0, 0), (0, 1), (1, 0), (1, 1), respectively. The stability of its equilibrium point was obtained from the local stability analysis of the Jacobian matrix obtained by the system, and the Jacobian matrix was as follows:(2)J=1−2pP2−A−C001−2pP1−E.According to the local stability analysis method of the Jacobian, the stability analysis of the above four equilibrium points is performed, as shown in Table2.Table 2
Local equilibrium point expression for the game process.
Equilibrium PointDeterminant ofJTraces ofJ(0, 0)P2 − A − CP1 − E(0, 1)P2 − A − CE − P1(1, 0)A + C − P2P1 − E(1, 1)A + C − P2E − P1


## 3.2. Discussion of Game Model

The local stability of each equilibrium point obtained in Table2 was discussed in 4 cases:(1)
WhenP2 > A − C, P > E, that is, both J determinant and traces of J were timing, (0, 0) was an unstable point. (0, 1) and (1, 0) were two saddle points. (1, 1) was a stability point. Evolutionary game equilibrium strategy was (Subsidy, Active Execution), p = 1, q = 1. For central governments, the cost of subsidies was less than benefits. For local governments, the proceeds of positive execution outweigh the cost. The equilibrium at this time belongs to the pure strategy Nash equilibrium, and the strategy of both sides chose according to their own situation, which has nothing to do with the other side. (2) When P2 > A − C, P < E, that is, the symbols of the J determinant and the J traces were positive and negative times, respectively. (0, 0) and (1, 1) were two saddle points. (0, 1) was an unstable point. (1, 0) was a stability point. Evolutionary game equilibrium strategy was (Subsidy, Negative Execution), p = 1, q = 0. For central governments, the cost of subsidies was less than benefits. For local governments, active execution costs were more than revenue. (3) When P2 < A − C, P > E, that is, the symbols of the J determinant and J traces are negative and timing, respectively. (0, 0) and (1, 1) were two saddle points. (1, 0) was an unstable point. (0, 1) was a stability point. Evolutionary game equilibrium strategy was (Not Subsidy, Actively Execution), p = 0, q = 1. For central governments, the cost of no subsidies was less than income. For local governments, active implementation costs were less than income. (4) When P2 < A − C, P < E, that is, when the symbols of the J determinant and the J traces were negative, (0, 1) and (1, 1) were two saddle points. (1, 1) was an unstable point. (0, 0) was a stability point. Evolutionary game equilibrium strategy was (No Subsidy, Negative Execution), p = 0, q = 0. For central governments, the cost of no subsidies was less than income. For local governments, active execution costs less than income. The replication dynamic phase diagram of both parties is shown in Figure 1.Figure 1
Dynamic phase diagram of the scenario evolution game process.According to the above analysis, to make the domestic housing rental market healthy under the financial decentralization system, we need to realizeP2 > A − C and P > E, that is, the central government subsidies of local government and local governments should actively implement the relevant central policies, supporting the corresponding local support policies, establishing the rent and buy housing system, letting all the people have houses to live in.


## 4. Empirical Analysis

The research data came from the Wind financial database, Chinese real estate index database, and the database of statistics bureau of municipal municipalities, provincial capitals, and separately planned cities. Covering 33 provincial capitals, planned cities, and municipalities from 2014–2019 (excluding Hong Kong, Macao, and Taiwan), there are 31 provincial capitals and municipalities directly under the central government, and 5 separately planned cities, including Shenzhen, Ningbo, Dalian, Qingdao, and Xiamen. Among them, the missing permanent population statistics in Changchun, Zhengzhou, and Lasa were eliminated, and the final number of sample cities was 33. We have done an empirical test through the panel data regression model.


### 4.1. Variable Measurement

#### 4.1.1. The Explained Variable

In the existing studies, Mayer et al. chose the rent level as the explained variable [15, 16]. However, the lack of comprehensiveness was first that from the perspective of regional differences, as different regions have different economic and social development, rental level could not measure the burden of local residents on housing rental clearly. While rent-income ratio (RENT/INC) could depict the correlation between the development level of local economics and the housing rental market [17, 18]. Secondly, different habits between different individuals lead to inconsistent preferences for rent or housing, rigid demand or investment [19], and the rent level could not reflect consumer behavior preferences. Therefore, this paper selected the rent-income ratio (RENT/INC) as the explained variable.


#### 4.1.2. The Core Interpretation Variable

This paper collected central-to-local special transfer payments for the years 2014–2019 of each region on the official website of the National Treasury (The special expenditure of the central government was divided into budgets and final accounts. The final account data were only released at the end of the year and published in the half of the second year, which has a certain lag for policy implementation. It was more reasonable to consider the budget number at the beginning of this year).Except for municipalities directly under the Central Government and cities separately in the plan, the statistical caliber of the central government for local special transfer payments was only available to the provincial level. For the subsidies of provincial cities, this paper was determined by multiplying a coefficiente. For the choice of coefficient e, we considered the basic logic of housing rental subsidies for cities which was the comprehensive proportion of the population and other aspects. Floating population ratio generally has two algorithms, firstly, the ratio of the floating population = 1 − (the number of urban registered population/total urban population at the end of the year), so that the proportion of the floating population can only be calculated at the provincial or municipal level, and the proportion of the population in the provincial capital city could not be investigated. Secondly, it is the ratio of the floating population = (Municipal permanent resident population-Municipal registered population)/(Provincial permanent resident population-provincial registered population). The result was positive for population inflow and conversely was outflow [20]. However, there would be inconsistent provincial and municipal symbols, or the two levels were negative, so the calculation proportion would be biased (For example, in 2019, Hebei Province net population was outflow, but Shijiazhuang was a net inflow of population. Fujian was the net inflow of population, but Xiamen was the net population inflow. Guangxi and Nanning were both net population outflow). Therefore, the permanent population [21, 22] was used to describe the coefficient e. That is, the number of e = municipal permanent population/provincial permanent population (0 < e < 1). For example, in 2015, the permanent population in Guangzhou accounted was 12.44% of Guangdong Province, so e = 12.44%. We used the central budget to local special transfer payments multiplied by the coefficient e to get TRAN to measure the central financial incentive to the local housing rental market. We have done this because the central and local transfer payments were made to the provincial level, and the sample of the empirical analysis in this paper was the municipal housing rental development. Therefore, after the provincial governments got the subsidies from the central finance, there were “secondary transfer payments” to the municipal finance. According to our collection of urban housing rental subsidy management measures in each province (Specifically, the Notice of Subsidy Funds for Some Central Government Finance in 2020 in Zhejiang Province, Public Notice of Advance Distribution Plan, and Performance Objectives of Subsidy Funds for Some Central Government Finance in 2020 in Guangdong Province, Opinions on Promoting Family Rental Subsidy for Urban Housing Guarantee in Shandong Province, etc), the provincial finance of municipal finance in housing rental subsidies mainly considers the floating population. For example, Guangdong Province mainly considered the affordable housing construction tasks for affordable housing construction subsidies, and the construction tasks directly related to the local floating population (For example, Guangdong Province Announcement on the Early Distribution Plan and Performance Target of some central government urban affordable housing project subsidy funds in 2020). In addition, from the perspective of relevant literature, the tasks of housing rental construction were mainly based on the number of the floating population [23]. So the subsidies for different cities were mainly compared by the proportion of the local floating population.


#### 4.1.3. The Mediation Variable

This paper chose the housing security budget expenditure (EXP) in the special expenditure of local public finance to measure the local government’s support to the development of the housing rental market.


#### 4.1.4. Controlling Variables

Controlling variables in this paper mainly include financial institution loan balance (LOAN), local actual GDP (GDP), resident consumer price index (CPI), whether it was housing rental pilot city (TEST), real estate residential investment (INVEST), or total resident provident fund (FUND).The interpretation and descriptive statistics of the variables are shown in Tables3 and 4, respectively.Table 3
Variable interpretation table.
Variable categoryVariableSymbolInterpretationThe explained variableRent-income ratioRENT/INCAverage rental price/per capita disposable incomeThe core interpretation variableCentral subsidiesTRSNSpecial transfer payments for the central budgetThe mediation variableLocal implementation effortsEXPLocal housing security budget expenditureReal estate residential investmentINVESTReal estate residential investment amountHousing rental pilot cityTESTWhether it is a housing rentalControlling variablesLocal actual GDPGDPGDPResident consumer price indexCPICPITotal resident provident fundFUNDTotal resident provident fundFinancial institution loan balanceLOANBalance of RMB loans of financial institutionsVariable categoryUnit of variableDescriptionThe explained variableYuan/one thousand yuanResidents’ rental preferenceThe core interpretation variableOne hundred million yuanCentral government support effortsThe mediation variableOne hundred million yuanLocal implementation effortsOne hundred million yuanActivity of local real estate marketVirtual variableImpact of the pilot housing rental policyControlling variablesTen billion yuanLocal economic development level—Local inflation levelsOne hundred million yuanOther income conditions of the residentsTen billion yuanActivity of local financialTable 4
Descriptive statistics of variables.
VariablesNMeanStandard deviationMinimum valueMaximum valueRENT/INC1980.660.300.011.50TRAN198102.48100.980.96616.05EXP19852.7471.954.66556.55INVEST1981017.99661.58120.293246.77TEST1980.080.2801GDP1989742.507855.851131.6238155.32CPI198102.060.63100.40103.40FUND198467.50638.8424.073830.12LOAN1985.000.693.636.61


### 4.2. Empirical Model Establishment and Regression Analysis

This paper studies the effect of finance on the incentive of the development of the housing rental market under the perspective of financial decentralization. According to the above analysis, there was a certain game between the central and local governments, and the subsidy from the central finance on the development of housing rental markets in local regions was transmitted to them through local governments. Then we can consider this idea that central finance has a brief effect on the development of the local housing rental market. In other words, the local implementation efforts serve as the intermediary variable of the central finance on the development of the local housing rental market. Therefore, the mediation effect model could be constructed as follows:(3)RENTINCit=α1+β1TRANit+γ1CONTROLit+μ1+ε1,EXPit=α2+β2TRANit+γ2CONTROLit+μ2+ε2,RENTINCit=α3+β3TRANit+χ3EXPit+γ3CONTROLit+μ3+ε3.Among them,i expressed city, t expressed time, CONTROL was all-controlling variables, αi, βi, γi, χi expressed the regression coefficient of each explanatory variables and control variables, μi expressed fixed effect, and εit expressed regression residual term.To avoid estimation deviations from different dimensions of each variable, we dealt with the data by adding 1 and took the logarithm [24]. The panel data fixed effect model was used for regression analysis, and the regression results are shown in Table 5.Table 5
Panel data regression results.
VariablesModel 4.1Model 4.2Model 4.3The explained variable: RENT/INCThe explained variable: EXPThe explained variable: RENT/INCTRAN−0.021∗ (−1.77)−0.021∗ (−1.84)−0.111∗ (−2.14)−0.111∗ (−2.11)−0.021∗ (−1.81)−0.021∗ (−1.89)EXP0.034∗ (2.38)0.036∗ (2.61)GDP−0.226∗ (−2.30)−0.161 (−1.65)−0.178 (−0.22)0.080 (0.09)−0.228∗ (−2.41)−0.161∗ (1.74)FUND−0.058 (−0.62)0.030 (0.32)0.008 (0.01)0.233 (0.28)−0.037 (−0.41)0.054 (0.59)LOAN0.177∗ (1.76)0.234∗ (2.04)0.062 (0.07)−0.008 (−0.01)0.132 (1.34)0.194∗ (1.75)INVEST−0.024 (−0.42)0.226 (−0.45)−0.032 (−0.58)CPI−0.542 (−0.31)10.547 (0.70)−0.552 (−0.34)TEST−0.066∗∗ (−2.71)−0.258 (−1.20)−0.068∗∗ (−2.90)C2.158 (3.29)3.596 (0.46)5.285 (0.96)−44.954 (−0.66)2.200 (3.47)3.684 (0.50)N198198198198198198R20.3020.3380.2800.2060.2820.275Note. The numbers in parentheses were the value oft; ∗, ∗∗, ∗∗∗ were significant at 10%, 5%, and 1% levels, respectively.


### 4.3. The Interpretation of Regression Result

(1)
In model 4.1, the variable (TRAN) regression results for the central finance supporting housing rental development were significant, indicating that the overall effect was obvious. We could make a point that the intermediary effect would exist [25]. The negative factor showed that the higher the fiscal subsidy was, the lower the rent-income ratio was. It may be explained as follows. Firstly, housing rental has certain public welfare attributes in the process of development. The higher the fiscal income was, the greater the support for the development of local public welfare business was and the more space for enterprises could reduce the rent pricing. At the same time, it could be noted whether the housing rental pilot city (TEST) has a certain role in supporting the development of housing rental. Therefore, the results also have certain support for the conclusion of this article, that is, the central government guides “Houses are used to live in, not to be used for speculation” and “the same right to lease and purchase” has a certain promotion effect on the development of the housing rental market.(2)
In model 4.2, the results were significant for variable (TRAN) regression supporting housing rental development. And in model 4.3, local execution strength (EXP) regression was significant, indicating that the mediation effect was obvious. The negative factor showed that the more subsidies the central government provides for local housing rental, the implementation of local financial policies would be weakened, showing that the local government would weaken the financial subsidies for the development of the local housing rental market. According to the game process of part 3 of this paper, the most idealized balance between the central government subsidies for the local housing rental market and the local housing could actively implement the policies of the central government. However, under the existing system, local governments shoulder major tasks such as local economic development, prompting the “promotion championship” model among local officials around GDP growth [13]. Therefore, it could be understood that the local government funding for the development of the local housing rental market was determined, and when the central subsidies increase, the local subsidies will be reduced accordingly.(3)
In model 4.3, the regression result of local implementation strength (TRAN) was significant. The symbols ofβ2χ3 and β3 were the same, indicating that a partial intermediary effect existed, which accounted for 17.97% and 19.03% of the total effect, respectively. Under the background of central financial decentralization, the development of the housing rental market needed central and local mutual cooperation together. The central government provides certain policies and financial subsidies; the local government actively carries out the relevant policies of the central implementation, makes the stable and healthy development of the housing rental market, and ensures the whole society “A sense of housing” [26].


## 4.1. Variable Measurement

### 4.1.1. The Explained Variable

In the existing studies, Mayer et al. chose the rent level as the explained variable [15, 16]. However, the lack of comprehensiveness was first that from the perspective of regional differences, as different regions have different economic and social development, rental level could not measure the burden of local residents on housing rental clearly. While rent-income ratio (RENT/INC) could depict the correlation between the development level of local economics and the housing rental market [17, 18]. Secondly, different habits between different individuals lead to inconsistent preferences for rent or housing, rigid demand or investment [19], and the rent level could not reflect consumer behavior preferences. Therefore, this paper selected the rent-income ratio (RENT/INC) as the explained variable.


### 4.1.2. The Core Interpretation Variable

This paper collected central-to-local special transfer payments for the years 2014–2019 of each region on the official website of the National Treasury (The special expenditure of the central government was divided into budgets and final accounts. The final account data were only released at the end of the year and published in the half of the second year, which has a certain lag for policy implementation. It was more reasonable to consider the budget number at the beginning of this year).Except for municipalities directly under the Central Government and cities separately in the plan, the statistical caliber of the central government for local special transfer payments was only available to the provincial level. For the subsidies of provincial cities, this paper was determined by multiplying a coefficiente. For the choice of coefficient e, we considered the basic logic of housing rental subsidies for cities which was the comprehensive proportion of the population and other aspects. Floating population ratio generally has two algorithms, firstly, the ratio of the floating population = 1 − (the number of urban registered population/total urban population at the end of the year), so that the proportion of the floating population can only be calculated at the provincial or municipal level, and the proportion of the population in the provincial capital city could not be investigated. Secondly, it is the ratio of the floating population = (Municipal permanent resident population-Municipal registered population)/(Provincial permanent resident population-provincial registered population). The result was positive for population inflow and conversely was outflow [20]. However, there would be inconsistent provincial and municipal symbols, or the two levels were negative, so the calculation proportion would be biased (For example, in 2019, Hebei Province net population was outflow, but Shijiazhuang was a net inflow of population. Fujian was the net inflow of population, but Xiamen was the net population inflow. Guangxi and Nanning were both net population outflow). Therefore, the permanent population [21, 22] was used to describe the coefficient e. That is, the number of e = municipal permanent population/provincial permanent population (0 < e < 1). For example, in 2015, the permanent population in Guangzhou accounted was 12.44% of Guangdong Province, so e = 12.44%. We used the central budget to local special transfer payments multiplied by the coefficient e to get TRAN to measure the central financial incentive to the local housing rental market. We have done this because the central and local transfer payments were made to the provincial level, and the sample of the empirical analysis in this paper was the municipal housing rental development. Therefore, after the provincial governments got the subsidies from the central finance, there were “secondary transfer payments” to the municipal finance. According to our collection of urban housing rental subsidy management measures in each province (Specifically, the Notice of Subsidy Funds for Some Central Government Finance in 2020 in Zhejiang Province, Public Notice of Advance Distribution Plan, and Performance Objectives of Subsidy Funds for Some Central Government Finance in 2020 in Guangdong Province, Opinions on Promoting Family Rental Subsidy for Urban Housing Guarantee in Shandong Province, etc), the provincial finance of municipal finance in housing rental subsidies mainly considers the floating population. For example, Guangdong Province mainly considered the affordable housing construction tasks for affordable housing construction subsidies, and the construction tasks directly related to the local floating population (For example, Guangdong Province Announcement on the Early Distribution Plan and Performance Target of some central government urban affordable housing project subsidy funds in 2020). In addition, from the perspective of relevant literature, the tasks of housing rental construction were mainly based on the number of the floating population [23]. So the subsidies for different cities were mainly compared by the proportion of the local floating population.


### 4.1.3. The Mediation Variable

This paper chose the housing security budget expenditure (EXP) in the special expenditure of local public finance to measure the local government’s support to the development of the housing rental market.


### 4.1.4. Controlling Variables

Controlling variables in this paper mainly include financial institution loan balance (LOAN), local actual GDP (GDP), resident consumer price index (CPI), whether it was housing rental pilot city (TEST), real estate residential investment (INVEST), or total resident provident fund (FUND).The interpretation and descriptive statistics of the variables are shown in Tables3 and 4, respectively.Table 3
Variable interpretation table.
Variable categoryVariableSymbolInterpretationThe explained variableRent-income ratioRENT/INCAverage rental price/per capita disposable incomeThe core interpretation variableCentral subsidiesTRSNSpecial transfer payments for the central budgetThe mediation variableLocal implementation effortsEXPLocal housing security budget expenditureReal estate residential investmentINVESTReal estate residential investment amountHousing rental pilot cityTESTWhether it is a housing rentalControlling variablesLocal actual GDPGDPGDPResident consumer price indexCPICPITotal resident provident fundFUNDTotal resident provident fundFinancial institution loan balanceLOANBalance of RMB loans of financial institutionsVariable categoryUnit of variableDescriptionThe explained variableYuan/one thousand yuanResidents’ rental preferenceThe core interpretation variableOne hundred million yuanCentral government support effortsThe mediation variableOne hundred million yuanLocal implementation effortsOne hundred million yuanActivity of local real estate marketVirtual variableImpact of the pilot housing rental policyControlling variablesTen billion yuanLocal economic development level—Local inflation levelsOne hundred million yuanOther income conditions of the residentsTen billion yuanActivity of local financialTable 4
Descriptive statistics of variables.
VariablesNMeanStandard deviationMinimum valueMaximum valueRENT/INC1980.660.300.011.50TRAN198102.48100.980.96616.05EXP19852.7471.954.66556.55INVEST1981017.99661.58120.293246.77TEST1980.080.2801GDP1989742.507855.851131.6238155.32CPI198102.060.63100.40103.40FUND198467.50638.8424.073830.12LOAN1985.000.693.636.61


## 4.1.1. The Explained Variable

In the existing studies, Mayer et al. chose the rent level as the explained variable [15, 16]. However, the lack of comprehensiveness was first that from the perspective of regional differences, as different regions have different economic and social development, rental level could not measure the burden of local residents on housing rental clearly. While rent-income ratio (RENT/INC) could depict the correlation between the development level of local economics and the housing rental market [17, 18]. Secondly, different habits between different individuals lead to inconsistent preferences for rent or housing, rigid demand or investment [19], and the rent level could not reflect consumer behavior preferences. Therefore, this paper selected the rent-income ratio (RENT/INC) as the explained variable.


## 4.1.2. The Core Interpretation Variable

This paper collected central-to-local special transfer payments for the years 2014–2019 of each region on the official website of the National Treasury (The special expenditure of the central government was divided into budgets and final accounts. The final account data were only released at the end of the year and published in the half of the second year, which has a certain lag for policy implementation. It was more reasonable to consider the budget number at the beginning of this year).Except for municipalities directly under the Central Government and cities separately in the plan, the statistical caliber of the central government for local special transfer payments was only available to the provincial level. For the subsidies of provincial cities, this paper was determined by multiplying a coefficiente. For the choice of coefficient e, we considered the basic logic of housing rental subsidies for cities which was the comprehensive proportion of the population and other aspects. Floating population ratio generally has two algorithms, firstly, the ratio of the floating population = 1 − (the number of urban registered population/total urban population at the end of the year), so that the proportion of the floating population can only be calculated at the provincial or municipal level, and the proportion of the population in the provincial capital city could not be investigated. Secondly, it is the ratio of the floating population = (Municipal permanent resident population-Municipal registered population)/(Provincial permanent resident population-provincial registered population). The result was positive for population inflow and conversely was outflow [20]. However, there would be inconsistent provincial and municipal symbols, or the two levels were negative, so the calculation proportion would be biased (For example, in 2019, Hebei Province net population was outflow, but Shijiazhuang was a net inflow of population. Fujian was the net inflow of population, but Xiamen was the net population inflow. Guangxi and Nanning were both net population outflow). Therefore, the permanent population [21, 22] was used to describe the coefficient e. That is, the number of e = municipal permanent population/provincial permanent population (0 < e < 1). For example, in 2015, the permanent population in Guangzhou accounted was 12.44% of Guangdong Province, so e = 12.44%. We used the central budget to local special transfer payments multiplied by the coefficient e to get TRAN to measure the central financial incentive to the local housing rental market. We have done this because the central and local transfer payments were made to the provincial level, and the sample of the empirical analysis in this paper was the municipal housing rental development. Therefore, after the provincial governments got the subsidies from the central finance, there were “secondary transfer payments” to the municipal finance. According to our collection of urban housing rental subsidy management measures in each province (Specifically, the Notice of Subsidy Funds for Some Central Government Finance in 2020 in Zhejiang Province, Public Notice of Advance Distribution Plan, and Performance Objectives of Subsidy Funds for Some Central Government Finance in 2020 in Guangdong Province, Opinions on Promoting Family Rental Subsidy for Urban Housing Guarantee in Shandong Province, etc), the provincial finance of municipal finance in housing rental subsidies mainly considers the floating population. For example, Guangdong Province mainly considered the affordable housing construction tasks for affordable housing construction subsidies, and the construction tasks directly related to the local floating population (For example, Guangdong Province Announcement on the Early Distribution Plan and Performance Target of some central government urban affordable housing project subsidy funds in 2020). In addition, from the perspective of relevant literature, the tasks of housing rental construction were mainly based on the number of the floating population [23]. So the subsidies for different cities were mainly compared by the proportion of the local floating population.


## 4.1.3. The Mediation Variable

This paper chose the housing security budget expenditure (EXP) in the special expenditure of local public finance to measure the local government’s support to the development of the housing rental market.


## 4.1.4. Controlling Variables

Controlling variables in this paper mainly include financial institution loan balance (LOAN), local actual GDP (GDP), resident consumer price index (CPI), whether it was housing rental pilot city (TEST), real estate residential investment (INVEST), or total resident provident fund (FUND).The interpretation and descriptive statistics of the variables are shown in Tables3 and 4, respectively.Table 3
Variable interpretation table.
Variable categoryVariableSymbolInterpretationThe explained variableRent-income ratioRENT/INCAverage rental price/per capita disposable incomeThe core interpretation variableCentral subsidiesTRSNSpecial transfer payments for the central budgetThe mediation variableLocal implementation effortsEXPLocal housing security budget expenditureReal estate residential investmentINVESTReal estate residential investment amountHousing rental pilot cityTESTWhether it is a housing rentalControlling variablesLocal actual GDPGDPGDPResident consumer price indexCPICPITotal resident provident fundFUNDTotal resident provident fundFinancial institution loan balanceLOANBalance of RMB loans of financial institutionsVariable categoryUnit of variableDescriptionThe explained variableYuan/one thousand yuanResidents’ rental preferenceThe core interpretation variableOne hundred million yuanCentral government support effortsThe mediation variableOne hundred million yuanLocal implementation effortsOne hundred million yuanActivity of local real estate marketVirtual variableImpact of the pilot housing rental policyControlling variablesTen billion yuanLocal economic development level—Local inflation levelsOne hundred million yuanOther income conditions of the residentsTen billion yuanActivity of local financialTable 4
Descriptive statistics of variables.
VariablesNMeanStandard deviationMinimum valueMaximum valueRENT/INC1980.660.300.011.50TRAN198102.48100.980.96616.05EXP19852.7471.954.66556.55INVEST1981017.99661.58120.293246.77TEST1980.080.2801GDP1989742.507855.851131.6238155.32CPI198102.060.63100.40103.40FUND198467.50638.8424.073830.12LOAN1985.000.693.636.61


## 4.2. Empirical Model Establishment and Regression Analysis

This paper studies the effect of finance on the incentive of the development of the housing rental market under the perspective of financial decentralization. According to the above analysis, there was a certain game between the central and local governments, and the subsidy from the central finance on the development of housing rental markets in local regions was transmitted to them through local governments. Then we can consider this idea that central finance has a brief effect on the development of the local housing rental market. In other words, the local implementation efforts serve as the intermediary variable of the central finance on the development of the local housing rental market. Therefore, the mediation effect model could be constructed as follows:(3)RENTINCit=α1+β1TRANit+γ1CONTROLit+μ1+ε1,EXPit=α2+β2TRANit+γ2CONTROLit+μ2+ε2,RENTINCit=α3+β3TRANit+χ3EXPit+γ3CONTROLit+μ3+ε3.Among them,i expressed city, t expressed time, CONTROL was all-controlling variables, αi, βi, γi, χi expressed the regression coefficient of each explanatory variables and control variables, μi expressed fixed effect, and εit expressed regression residual term.To avoid estimation deviations from different dimensions of each variable, we dealt with the data by adding 1 and took the logarithm [24]. The panel data fixed effect model was used for regression analysis, and the regression results are shown in Table 5.Table 5
Panel data regression results.
VariablesModel 4.1Model 4.2Model 4.3The explained variable: RENT/INCThe explained variable: EXPThe explained variable: RENT/INCTRAN−0.021∗ (−1.77)−0.021∗ (−1.84)−0.111∗ (−2.14)−0.111∗ (−2.11)−0.021∗ (−1.81)−0.021∗ (−1.89)EXP0.034∗ (2.38)0.036∗ (2.61)GDP−0.226∗ (−2.30)−0.161 (−1.65)−0.178 (−0.22)0.080 (0.09)−0.228∗ (−2.41)−0.161∗ (1.74)FUND−0.058 (−0.62)0.030 (0.32)0.008 (0.01)0.233 (0.28)−0.037 (−0.41)0.054 (0.59)LOAN0.177∗ (1.76)0.234∗ (2.04)0.062 (0.07)−0.008 (−0.01)0.132 (1.34)0.194∗ (1.75)INVEST−0.024 (−0.42)0.226 (−0.45)−0.032 (−0.58)CPI−0.542 (−0.31)10.547 (0.70)−0.552 (−0.34)TEST−0.066∗∗ (−2.71)−0.258 (−1.20)−0.068∗∗ (−2.90)C2.158 (3.29)3.596 (0.46)5.285 (0.96)−44.954 (−0.66)2.200 (3.47)3.684 (0.50)N198198198198198198R20.3020.3380.2800.2060.2820.275Note. The numbers in parentheses were the value oft; ∗, ∗∗, ∗∗∗ were significant at 10%, 5%, and 1% levels, respectively.


## 4.3. The Interpretation of Regression Result

(1)
In model 4.1, the variable (TRAN) regression results for the central finance supporting housing rental development were significant, indicating that the overall effect was obvious. We could make a point that the intermediary effect would exist [25]. The negative factor showed that the higher the fiscal subsidy was, the lower the rent-income ratio was. It may be explained as follows. Firstly, housing rental has certain public welfare attributes in the process of development. The higher the fiscal income was, the greater the support for the development of local public welfare business was and the more space for enterprises could reduce the rent pricing. At the same time, it could be noted whether the housing rental pilot city (TEST) has a certain role in supporting the development of housing rental. Therefore, the results also have certain support for the conclusion of this article, that is, the central government guides “Houses are used to live in, not to be used for speculation” and “the same right to lease and purchase” has a certain promotion effect on the development of the housing rental market.(2)
In model 4.2, the results were significant for variable (TRAN) regression supporting housing rental development. And in model 4.3, local execution strength (EXP) regression was significant, indicating that the mediation effect was obvious. The negative factor showed that the more subsidies the central government provides for local housing rental, the implementation of local financial policies would be weakened, showing that the local government would weaken the financial subsidies for the development of the local housing rental market. According to the game process of part 3 of this paper, the most idealized balance between the central government subsidies for the local housing rental market and the local housing could actively implement the policies of the central government. However, under the existing system, local governments shoulder major tasks such as local economic development, prompting the “promotion championship” model among local officials around GDP growth [13]. Therefore, it could be understood that the local government funding for the development of the local housing rental market was determined, and when the central subsidies increase, the local subsidies will be reduced accordingly.(3)
In model 4.3, the regression result of local implementation strength (TRAN) was significant. The symbols ofβ2χ3 and β3 were the same, indicating that a partial intermediary effect existed, which accounted for 17.97% and 19.03% of the total effect, respectively. Under the background of central financial decentralization, the development of the housing rental market needed central and local mutual cooperation together. The central government provides certain policies and financial subsidies; the local government actively carries out the relevant policies of the central implementation, makes the stable and healthy development of the housing rental market, and ensures the whole society “A sense of housing” [26].


## 5. Conclusion and Relevant Recommendations

The development of housing rental plays a very important role in the implementation of “Houses are used to live in, not to be used for speculation,” calming housing prices and stabilizing the real estate market. This paper evaluates the effectiveness of the relevant policy implementation through empirical analysis, and the next step should be improved from the following aspects.Firstly, it was supposed to actively guide the long-term development of the housing rental market through top-level design. In recent years, China’s housing rental market has been developing continuously, which has played an important role in accelerating the improvement of housing conditions for urban residents and promoting the new urbanization process. However, problems such as insufficient development of market suppliers, irregular market order, and imperfect laws and regulations were still relatively prominent. Focusing on high-quality development and high-quality life, to meet the people’s demand for rental housing, to accelerate the introduction of top-level design documents for the housing security system at the national level, further clarify the division of labor between the central and local governments and between various departments, improving a basic framework for the development of the housing rental market, and improving the corresponding supervision mechanism. The government would provide policy support to guide investment in multisubject investment and multichannel supply and improving the housing security system with public rental housing, affordable rental housing, and housing with shared property rights. Local governments should formulate and improve supporting systems in response to local conditions, formulate scientific plans for special subsidies for housing rental, do a good job in postevent evaluation and supervision, ensure that special subsidy funds are used for use, and promote the sound and healthy development of the housing rental market.Secondly, it was supposed to support the rapid development of the housing rental market by increasing financial subsidies. As one of a variety of housing supply means of rent and purchase, the housing rental business has a certain guarantee and public welfare in the early stage of development. Therefore, increasing financial support was conducive to guiding the rapid and healthy development of the market. We would explore the establishment of a financial support policy system with multiple measures. In addition to direct subsidies, local governments may, in light of the actual market conditions, provide preferential tax policy support to housing rental enterprises, institutions, and individuals registered and filed in accordance with the law [27]. The central government subsidized local governments, and local governments actively implement relevant policies of the central government and supported corresponding local support policies. We would establish a postevaluation system for financial support, constantly optimize policies for financial support, and ensure that special financial funds for housing rental development can be “used to the best edge.”Thirdly, it was supposed to promote the healthy development of the housing rental market by improving the relevant laws and policies of housing rental. In the early stage of housing rental development, relevant policies and regulations were not perfect, market entities were mixed, and many market entities supervised arbitrage. Many places have even seen fictitious housing rental companies’ houses and run away after collecting tenant rent. Therefore, it was necessary to improve the relevant laws and policies of housing rental and strengthen supervision, which was promotion to the healthy development of the market. A normal management mechanism of housing leasing should be established. Local departments should establish coordination and linkage mechanisms and establish a multilevel disposal mechanism for housing rental disputes. We would accelerate the establishment of a unified housing rental management and service platform and improve the functions of institutional filing and opening reports, housing verification, information release, and online registration and filing. We would strengthen the management of business entities and strictly review the registration of market entities. To strengthen the management of housing source information release, the network information platform shall verify the qualification of housing source information release subject and the necessary information of housing sources so as to ensure the real entrustment, real status, and real price.



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*Source: 1252407-2021-10-12.xml*