Has the Newly Imposed Property Tax Controlled Housing Prices? An Analysis of China’s 2009–2020 Interprovincial Panel Data

Abstract

The stability of the real-estate market is crucial to China’s economic development and, in times of crisis, the economy will experience systemic adverse reactions that require appropriate regulation by the state using tax policy tools. Therefore, we analyzed the impact of real-property tax on house prices using panel data for 31 provinces in China from 2009 to 2020 using an empirical method, i.e., the instrumental variables approach. The empirical results show that each of the previous property-related taxes actually contributed to the increase in house prices and did not have a dampening effect. The newly introduced property tax will lead to a decline in house prices, which will help to alleviate the overheating of real-estate investment and mitigate the real-estate bubble crisis. A rational view of the impact of a property tax on housing prices needs to be taken in the context of factors such as income levels, consumer price levels, loan rates, and Chinese consumer culture. In order to achieve the goal of “no speculation in housing”, we also need to pay attention to the regulating effect of a property tax in combination with many other factors. This study is important for promoting property tax reform, curbing overheated real-estate investment, and promoting healthy economic development.

1. Introduction

China’s 2021 government report presents a plan for the smooth control and healthy development of the real-estate market, adheres to the positioning of housing without speculation, adopts initiatives such as equal rights to rent and purchase and city-specific policies, and establishes the goal of the balanced development of real estate, finance, and the real economy. In addition, in the 14th Five-Year Plan and the outline of the 2035 Visionary Goals, the promotion of property tax legislation was once again proposed. On 23 October 2021, the Standing Committee of the National People’s Congress of China adopted a decision on the pilot property tax, i.e., to conduct a trial levy of a property tax in some areas through which to accumulate practical experience for promoting property tax legislation. In the subsequent meeting in respect of China’s financial work, another plan was proposed to prepare for the property tax pilot. This information immediately aroused widespread concern and heated discussion in society, with consumers believing that the property tax will eventually be implemented and will also affect consumers’ psychological expectations. As a result, consumers with housing needs will choose to “wait and see”, expecting that the property tax will guide the market price down to an acceptable level. The reaction of consumers who are currently homeless and have immediate housing needs is likely to be even stronger. However, housing investors are more concerned about how much the cost of ownership will increase after the property tax is introduced. Current theory and the experience of countries that have already imposed property taxes suggest that the cost of owning multiple homes increases for investors, and that the more homes there are, the larger the area, and the heavier the tax burden is likely to be. This tends to lead to an increase in transaction costs for real-estate investment, which has a negative effect on the market. Whether the property tax can achieve curbing speculation in real estate is yet to be further studied.

Since 2006, the Chinese government has repeatedly proposed to control housing prices, curb the overheated growth of real-estate investment and the excessive rise in housing prices, and use tax, financial, land, and other policy tools to stabilize prices and regulate the market. Despite this, real-estate market prices are still running high, and the bubble economy is still huge. Compared to other countries, China’s real-estate market is very different because the land is owned by the state. For more than a decade, the Chinese government has relied on real-estate investment to drive economic growth, and many local governments, in particular, have relied on land-transfer revenue and property-related taxes for revenue, which are humorously referred to as “land finance”. In China, housing prices affect the vital interests of the government, real-estate enterprises, banks, and consumers, which subsequently affect the whole economy. Therefore, it is necessary to study the relationship between real-estate tax and property prices for China’s current economic transformation. In fact, the price of real estate is greatly influenced by policy regulation and is often used as a “chamber pot” to stimulate the short-term economy and supplement local finances. Now, the Chinese government expects to levy a property tax to control property prices, return to market rationality, bring the real-estate market back to its intended function, weaken its financial attributes, and even end the “chamber pot” theory of real estate. The authors are currently faced with the following questions: how will the property tax affect housing prices and how much will it do so?

In this study, we examine the impact of a property tax on housing prices using panel data for 31 provinces and regions in China during the period 2009–2020, and further analyze the impact of the current property tax and the yet-to-be-imposed property tax on housing prices. The authors also explore the effects of related factors such as average urban wages, loan interest rates, and economic uncertainty, and conduct robustness and endogeneity tests to make our findings credible. Compared with the existing literature, the possible contributions of our study are as follows. Firstly, the authors use the property tax to be levied as a quasinatural experiment by which to explore the causal effect of the future property tax on housing prices and to clear the theoretical misconception that the property tax will not affect housing prices. Secondly, based on previous research, the authors extend the application of a property tax using the effective tax rate and property value, and extrapolate the results to the existing data to compensate for the lack of actual data in the existing literature. Thirdly, the authors use the urbanization rate, birth rate, natural population growth rate, and divorce rate as the instrumental variables of the housing consumption demand capacity; these better deal with the endogeneity of housing prices and demand and ensure the robustness and consistency of the empirical results. Fourthly, in China, the house as a symbol of the home is deeply rooted in people’s minds, and the concept of having a house in order to have roots influences consumers’ home purchase decisions. This paper reveals the impact of China’s unique property culture on housing prices and government regulation.

In Section 2, we review the literature on the relationship between housing taxes and prices and present the research hypotheses. In Section 3, the authors provide a detailed empirical analysis. In Section 4, the authors detail the empirical outcomes and discussions. Finally, in Section 5, the authors present our conclusions.

2. Literature Review and Hypotheses

In the current Chinese context, housing has both commodity and financial attributes, with the latter being stronger. These two different attributes can lead to significant behavioral differences between housing consumers and investors, and even opposite reactions. In general, housing consumers are more concerned about the acquisition cost and housing attributes, while investors are more concerned about the holding risk and investment returns. In recent years, the booming real-estate industry has led both consumers and investors to have higher expectations for housing appreciation and to therefore pay more attention to housing regulation policies, especially property tax. What impact the introduction of a property tax has on housing prices is a matter of great concern to theoretical researchers, policymakers, and even the general public. One of the core issues is the price of real-estate transactions and its future trend after the introduction of the property tax. Studies have shown that large and small changes in housing prices directly affect consumers’ psychology and consumption decisions [1]. When housing prices are unstable and trending downward, changes in market sentiment can cause investors to invest less and some speculative capital to flow out of the real-estate market and into other financial markets. However, when housing prices are stable and trending upward, changes in supply and demand will lead consumers to increase their desire to buy, or even to rush to buy, or else to “wait and see”, which will also lead housing investors to be optimistic about the market and potentially continue to make additional investments. In this case, the market “speculation” behavior will continue to influence the market, real-estate financial attributes will be greater, and the “market bubble” burst risk will also increase.

It is true that the highly anticipated property tax has been discussed for years, and its introduction and legislation are on the Chinese government’s agenda. As a matter of fact, property tax is not a newly derived tax in China, as it was clearly stipulated in the provisional regulations of property tax in 1951. Later, with changes in the economic environment and system, the property tax has evolved several times, and its specific content is somewhat different from the property tax the authors are discussing now. At present, the property tax emphasizes the series of taxes related to the real-estate economy, especially the inclusion of personal housing in the taxation scope, which can be classified as a tax comprising both capital gain and the nature of the property. Of course, a property tax will involve many aspects; there is a lack of data in respect of actual collection and management in China, and the economic effects and nature of the tax after its introduction are still at the stage of scattered and fragmented discussions.

Nevertheless, many scholars have launched discussions and elaborations on the impact of the property tax on housing prices and the economy, and the mainstream view is that the introduction of the property tax will lead to a decline in housing prices [2,3]. In the case of the pilot individual housing property tax in Shanghai and Chongqing in 2011, the average housing price in Shanghai declined due to the property tax, while the housing price in Chongqing showed an opposite upward movement, which indicates the heterogeneity of housing prices between regional cities [4]. In general, the average consumer can afford to buy a home only with the help of a housing provident fund or commercial loans, or even with the savings of several generations, and some people need to borrow from friends and relatives to raise the down payment. Therefore, in addition to the down-payment ratio and interest rates of loans, the most important concern is how the property tax will affect housing prices [5,6]. In particular, over the last fifteen years, housing prices have risen, and the increase has exceeded the growth in average wages, which has led to a phenomenon where society is looking forward to housing and speculation. There are many reasons for the high housing prices, among which the significant impacts are a result of the high land cost and high tax cost [7]. High housing prices indirectly lead to a consequent increase in labor costs in various industries, which eventually causes a decrease in the tax contribution and profitability of enterprises and a mismatch of resources, which in turn accelerates the flight of labor from cities with high housing prices and, ultimately, reduces the economic growth rate [8,9,10,11,12,13,14].

Moreover, the continued growth of housing prices will stimulate investors to make additional investments, giving them the advantage of owning more housing and wealth. At the same time, the appreciation of real estate can cause a further increase in market demand, which in turn will drive house prices to continue to rise. It will also cause the average homebuyer to save more for a house, use credit facilities, and even curtail nonessential consumption expenditure. This will affect the distribution of total social consumption power and tend to cause overconcentration of consumption. Moreover, people’s spending power in other areas will be diminished when a large amount of money is absorbed by the real-estate economy. The imbalance of consumption capacity will not only affect the market supply, but also generate real-estate wealth effects with undesirable consequences [1,15,16,17]. However, high housing prices can also be advantageous. High housing prices boost local finances, providing local governments with more money to spend on public services and capitalizing urban infrastructure, which boosts regional economies in the short term. However, both of these factors, together with the capitalization of property taxes, have in turn driven up housing prices. Obviously, the degree of impact again depends on the size of the capitalization rate, and the specific impact is subject to further study. The impact should at least be positive, and this also requires the strengthening of tax collection and administration to promote tax equity [2,18,19].

Of course, the core basis for the rise in housing prices is the presence of many people with a strong need for housing and speculators, especially in areas with rapid population growth [20,21]. In such areas, the real-estate market comprises a large number of people with immediate housing needs, especially young people, who urgently need to own their own homes due to marriage and family, and have a strong desire to buy even if the price of housing increases [22]. Some people will target business opportunities and select properties with good location and market potential in which to invest first and transfer at a later time to make a profit. However, there are some housing agencies and developers who jointly speculate on housing prices, and there are also unscrupulous businessmen who create false transaction information and disturb the order of the normal real-estate market. This information asymmetry and the wealth distribution differences will cause abnormal money flow and housing price fluctuations, which are another core feature of the high running trend of housing prices in recent years. Of course, the supply of the real-estate market in each region and the degree of development of the secondary housing market are also important factors influencing housing prices [23]. Therefore, housing prices need to be effectively regulated by the government through the introduction of corresponding fiscal, monetary, and financial policies in order to make them more healthy and stable. Moreover, different property tax policies will have different impacts on economic industrial development and its transfer changes [24,25,26].

In addition, there are other views on the relationship between property taxes and housing prices, with some scholars arguing that property taxes are beneficial in respect of their effect on public service expenditure, but are not beneficial in respect of housing prices [27]. This issue has been studied using a dynamic model and scholars have come to a different conclusion that property taxes have both beneficial and capital tax characteristics [28]. Moreover, the housing price effect is a reflection of the price that residents pay for public services (or products). It is possible for property taxes to be capitalized by regional differences in amenities and public services such as business environment, health, and education resources. Although some of the findings are based on data measured in the United States, in general, property taxes have a dual role [29,30,31,32,33]. Based on the above analysis, the authors propose Hypothesis 1.

Hypothesis 1. 

Under current conditions, the current property-related taxes have contributed to the rise in housing prices.

Since 2005, China’s real-estate market has been basically in a “golden age”, with an increasing per capita housing area and rising housing prices, and houses have become investments in most cases. For this reason, a quantitative model can be constructed by which to understand the relationship between real-estate investment and housing price changes. During the investment holding period, the real estate is rented out for additional income. The authors assume that the initial investment in housing is V, the annual housing rent is R, the discounted interest rate of the bank term deposit is r, the appreciation of property is A, and the income is P. Then, the investment return can be modeled as follows:

$$P=(R+A)-Vr$$

(1)

Assuming that the real-estate appreciation A is 0, it follows that R should be at least equal to Vr to be profitable, and its minimum sunk cost is the interest of bank deposits in the same period. However, in the golden age of real-estate investment, housing prices are on the rise, even though R is less than Vr, and the change in A has far exceeded the difference between R and Vr. This leads to the fact that housing investors will continue to be keen to increase their investment. Otherwise, when A is negative, R should be greater than the sum of Vr and A to be rational. For example, an investment of $4 million in a home in Beijing is held for external rental at an average annual market rent of $5000 and an average annual discount rate of 3% for a bank time-deposit. If the investment money exists in the bank, it can earn $120,000 a year in interest, which is much greater than the rental income. Only if the price of housing in Beijing keeps rising will there be a reasonable return on investment. Therefore, controlling the excessive rise in housing prices is the only way to gradually reduce the financial function of real estate and return housing to its real function.

In addition, the relationship between the impact of housing price changes on the macroeconomy has been receiving great attention from scholars. High housing prices push up the labor cost of enterprises, reduce the profitability of enterprises, and inhibit economic growth [8,10]. Moreover, rising housing prices cause a resource mismatch; reduce resource allocation efficiency; affect R&D investment and innovation output of enterprises; reduce individual entrepreneurship rates; and affect household investment, saving, and consumption decisions, which have an increasingly obvious effect on long-term economic growth [1,20,33,34,35,36]. In addition, persistently high housing prices will aggravate the continuous rise in land prices, attracting a large amount of speculative capital to real estate and creating a herding effect of speculation in real estate. In addition, inflated real estate rental market prices will lead to a consequent increase in commercial and residential rental costs, which in turn will reduce the profits of the real economy and other consumption expenditures of residents. Of course, factors such as consumers’ net income, the level of home mortgage interest rates, and the size of the down payment directly affect the ability to purchase housing [37]. Moreover, excessive real-estate acquisition costs may take away a large portion of household income, and the increase in property and energy prices after the purchase of a house increases the cost of living, which in turn affects house price changes and can also give rise to a new type of poverty group [38,39]. The rising costs of each industry will also drive up their incomes, interacting with each other to form an economic cycle. This is highly likely to give rise to a larger real-estate bubble [40,41,42,43,44]. Once a bubble crisis breaks out, it will trigger a chain reaction, creating a subprime mortgage crisis, which will have a huge impact on and cause damage to the economy. For example, in Japan in the 1990s and the United States around 2000, real-estate bubble bursts caused financial crises, economic recession, and other adverse effects. In order to reduce the risk of bursting, the government should introduce appropriate policies to moderate and control the bubble, and introduce property taxes and other regulatory tools to effectively shrink the bubble at the right time [45].

In order to reduce the above-mentioned risks, the property tax to be levied in China is estimated to differ little from the current taxation system for non-residential real estate, and the greater difference in its taxation impact is for personal residential housing, especially when a large number of natural persons are involved in taxation. In fact, the transfer, rental, investment, and gifting of houses to natural persons will now involve property tax, urban land tax, stamp duty, VAT, urban construction tax, personal income tax, and other related taxes. However, the property tax will reintegrate all the above taxes and, from this perspective, it can be classified as a new tax. The basic goals of the tax are to provide local governments with a tax source and provide financial support for public services [46].

Thus, following the approach assumed in Yinger’s bid-ranking model [47,48], housing expenditure (h), personal effects (e), and public goods/services (g) are the three main areas of consumption of interest to a household, and can be expressed in terms of the utility function as U (h, e, g). The budget constraint faced by the household is:

$$C=p(g,\tau )h+e+\tau V$$

(2)

$$V=p(g,\tau )h / r$$

(3)

then

$$C=e+p(g,\tau )h\times (1+\tau \ast )$$

(4)

where C is income, p is the unit price of housing, V is the value of real estate, r is the discount rate, τ is the effective tax rate, τV is the property tax, and τ^* = τ/r. Continuing with the derivation of the formula using property taxes and net income introduced separately, the results show that the taxability of property taxes has a negative relationship with the effective tax rate and the annual housing rent [18].

For a more in-depth study, based on the existing scholars’ measurement–result interval for the effective rate of a property tax, we take 0.5% as the benchmark tax rate for the effective rate of a property tax based on the measurement of income as the base indicator with reference to the research of Zhang and Hou (2016) on the taxability of a property tax [18]. Real-estate values are sensitive to changes in tax rates, and there are many factors affecting property tax rates. The government may also set a high nominal tax rate and then reduce the effective tax rate level through tax incentives and other forms, which are to be further studied later [43,49]. We use the effective tax rate to measure the value of real estate, and the result is the “property tax” in previous years, which may be different from the property tax to be implemented in the future. Especially in the short term, property taxes may have different effects on the decision-making behavior of housing consumers and investors, but in the long term such simulation estimates have some value and significance. In particular, under the double uncertainty of future income and housing price, homebuyers’ preferences, ages, and housing consumption plans will affect the change in the market supply and demand price, and even the phenomenon of “once released, prices will rise, once tightened, they will fall, and once fallen, they will be saved”. The authors propose another hypothesis:

Hypothesis 2. 

If other factors remain unchanged, the property tax to be introduced will suppress the rise in housing prices.

3. Method

To further verify whether the above hypothesis is correct, appropriate independent and dependent variables were screened based on the correlation and causality between a property tax and housing price, and relevant control variables were searched to reduce the error caused by interfering terms. Then, according to the empirical design, and after collection, collation, and cleansing of data, valid samples were processed to ensure the data were true and reliable.

3.1. Data Description

The authors used a sample of 31 provinces (autonomous regions and municipalities directly under the control of the central government) in China between 2009 and 2020 for the study data collection. Since property taxes have not been introduced or collected on different bases, the sample data for Hong Kong, Macao, and Taiwan were segregated in the study sample to enhance the homogeneity of the data. The authors mainly used the China Statistical Yearbook as the base source of data, combined with the China Stock Market & Accounting Research Database and the China Tax Yearbook. We also drew on the Economic Policy Uncertainty (EPU), an index of uncertainty in China’s economic policy compiled by Lu and Huang [50]. Because of missing data in individual provinces in individual years, the final study sample comprised provincial unbalanced panel data covering 12 years, 29 variables, and 384 observations. To eliminate the effect of extreme values, the continuous variables were Winsorized before and after 1%.

3.2. Model

In order to test the impact of a property tax on housing prices and infer the causal relationship between them, the following econometric model was established:

$$\begin{array}{c}{Y}_{it}(cpric{e}_{it},hpric{e}_{it})={\beta }_0+{\beta }_{1}propta{x}_{it}+{\beta }_{2}rta{x}_i{}_t+{{\displaystyle \sum {\beta }_{k}controls}}_{it}\\ +difex{p}_i+yea{r}_t+{\epsilon }_{\mathrm{it}}\end{array}$$

(5)

$$propta{x}_{it}=pta{x}_{it}+lan{t}_{it}+land{v}_{it}$$

(6)

$$rtax=value\times T$$

(7)

In the above model, i denotes province or region and t denotes year. The dependent variables Y are the sales price of commercial properties (cprice) and the sales price of residential commercial properties (hprice), respectively. The core independent variable is a property tax, which is divided into different taxpayers: the property tax with enterprises as the main taxpayers in practice (proptax) and the property tax that will be introduced to include individual housing (rtax). The control variables (controls) include other possible independent variables besides property tax; district and year denote regional province-fixed effects and time-fixed effects, respectively, as random error terms.

3.3. Description of Variable Construction

(1)

Commodity house sales price (cprice): The average sales price of commodity houses was selected as the basic explanatory variable, and the natural logarithm of the average sales price of commodity houses in different regions in different periods was taken with reference to the common practice of the existing literature.

(2)

Residential sales price (hprice): In order to further explore the impact of a property tax on the sales price of ordinary residential properties, the average sales price of residential properties was selected as the second explanatory variable and was also treated as a natural logarithm.

(3)

Property tax (proptax): This is the sum of property tax (ptax), land use tax (lant), and land value added tax (landv), which are related to real estate in current tax practice and are being levied and is a new property tax that is treated by taking the natural logarithm of its aggregated value.

(4)

Resident property tax (rtax): This refers to the existing studies and calculations; the approximate reference range of the effective tax rate (ETR) for property tax is 0.3–1% when the property value is used as the basis for taxation. In addition, the effective tax rate of 0.5% was measured against the property value to measure the residents’ ability to pay a property tax by referring to the research results of Zhang and Hou (2016) [18]. The natural logarithm of the imputed value was used to simulate the “property tax” in previous years, and the causal relationship between a property tax and housing price was inferred and analyzed using simulated data.

(5)

Control variables (controls): Consumption potential (cpotential), urbanization rate (urbanrate), birth rate (birthrate), natural population growth rate (popgrowth), divorce rate (divorce), housing loan interest rate (loanrate), total land operation income (landincome), land operation profit (landprofit), sales of commercial properties (comhs), sales of residential commercial properties (respro), average wage of employed persons (avwage), per capita consumption expenditure of urban residents (consp), per capita household savings rate (savrate), per capita income and expenditure balance of urban residents (difexp), inflation level (Inflation), GDP per capita (pergdp), and economic policy uncertainty index (epu) as the base control variables. The variables are defined in

Table 1. Variable description.

Variable Type	Abbreviation	Variables	Description and Operationalization
Dependent Variables	cprice	Commodity house sales price	The average sales price of commercial properties is taken as the natural logarithm
	hprice	Housing price	The average residential sales price is taken as the natural logarithm
Independent Variables	proptax	Property tax	The sum of property tax, land use tax, and land value-added tax
	rtax	Resident property tax	Resident property tax = ETR × completed property value (value)
Control Variables	cpotential	Consumption potential	The year-end population of the region is taken as the natural logarithm
	urbanrate	Urbanization rate	Urbanization rate = urban population/total population
	birthrate	Birth rate	Birth rate = annual number of births/annual average number of births × 1000‰
	popgrowth	Natural population growth rate	Natural population growth rate = (number of births-deaths during the year)/average annual number of people
	divorce	Divorce rate	Divorce rate = number of divorces/total population × 1000‰
	loanrate	Housing loan interest rate	Housing basic goods rate percent
	landincome	Total land operation income	The amount of total income generated from land operations is taken as the natural logarithm
	landprofit	Land operation profit	The amount of profit from land operation is taken as the natural logarithm
	compro	Sales of commercial properties	Sales of commercial properties are taken as the natural logarithm
	respro	Sales of residential commercial properties	Sales of residential commercial properties take the natural logarithm
	avwage	Average wage of employed persons	Total wages of employed persons divided by the number of employed persons
	consp	Per capita consumption expenditure of urban residents	Total urban consumption divided by population
	savrate	Per capita household savings rate	Household disposable income and expenditure per capita difference divided by income
	difincome	Difference between per capita income and expenditure of urban residents	Per capita income less expenditure of urban residents
	inflation	Inflation level	Urban consumer price index
	pergdp	GDP per capita	GDP divided by total population (previous year = 100)
	epu	Economic policy uncertainty index	Economic uncertainty index calculated from EPU indicators

.

In particular, it should be noted that, in a real sense, the property tax that China is about to start levying cannot be a simple merger of a property tax and urban land use tax, but a new tax derived from a reintegrated conversion. For this reason, the authors tried to measure the previous years’ property-related taxes as much as possible to form the de facto property tax amount as the primary core independent variable. More valuable for research is the simulated property tax value obtained using the effective tax rate of a property tax and real-estate value measurement based on the existing literature research results as the second core independent variable. Both were introduced into the aforementioned economic model separately to further examine the impact of property taxes on housing prices, combining factual and counterfactual variables to infer causality.

Table 2. Descriptive statistics of the variables.

Variables	Sample Size	Mean Value	Median	Standard Deviation	Maximum	Minimum	Dispersion Coefficient
cprice	384	8.742	8.658	0.506	10.54	7.805	0.0579
hprice	384	8.697	8.595	0.536	10.66	7.78	0.0617
proptax	384	5.243	5.204	1.938	12.35	−2.04	0.37
rtax	384	1.157	1.176	1.324	5.15	−4.3	1.144
comhs	384	7.489	7.581	1.468	12.06	1.645	0.196
respro	384	7.439	7.553	1.459	11.95	1.645	0.196
landincome	384	7.33	7.395	1.445	11.68	2.079	0.197
avwage	384	10.93	10.94	0.398	12.09	10.09	0.0364
urbaninc	384	10.21	10.24	0.372	11.24	9.387	0.0364
consp	384	9.832	9.866	0.348	10.78	9.081	0.0354
pergdp	384	10.68	10.67	0.498	12.01	9.289	0.0466
epu	384	7.535	7.313	0.496	8.452	7.008	0.0658
Inflation	384	101.8	101.9	2.434	110.3	89.84	0.0239
savrate	384	0.312	0.314	0.0496	0.448	0.201	0.159
cpotential	384	8.238	8.264	1.054	11.86	5.69	0.128
urbanrate	384	56.72	55.66	13.51	89.6	22.3	0.238
birthrate	375	7.622	7.41	2.4	14.57	2.31	0.315
popgrowth	381	2.776	2.48	3.316	60	−2.97	1.195
divorce	384	2.724	2.624	1	5.19	0.43	0.367

shows the descriptive statistics of the main variables. Some of the missing samples for birth rate and natural population growth rate in the table are mainly due to the fact that, during the period 2009–2012, in some years individual provinces did not collect relevant statistics on these indicators, so there are missing values in the statistical yearbook.

4. Results

According to the aforementioned empirical design scheme, based on the correlation and causality between a property tax and housing price, the relevant regressions were conducted, and the regression results were analyzed accordingly. In addition, robustness tests were conducted according to the empirical requirements to enhance the reliability of the empirical findings.

4.1. Baseline Regression Results Based on the Correlation between Property Taxes and Housing Prices

Estimated for the benchmark model of commodity house sales prices in Equation (5), the results of the benchmark regressions in

Table 3. Baseline regression results.

	OLS Benchmark Regression	Two-Way Fixed-Effects Regression
	(1)	(2)
proptax	0.0784 ***	0.0691 ***
	(10.5965)	(3.2063)
rtax	−0.2045 ***	−0.0381 **
	(−7.3977)	(−2.4437)
comhs	0.3545 ***	0.0415
	(2.8794)	(0.2903)
landincome	0.1381 ***	0.0288
	(2.7224)	(0.9032)
avwage	0.3363 ***	0.3761 ***
	(3.5568)	(4.2693)
pergdp	0.4631 ***	−0.0747
	(7.6276)	(−1.0833)
epu	−0.0079	0.0858 ***
	(−0.2351)	(5.7415)
Inflation	−0.0044	0.0059 ***
	(−1.0053)	(3.8623)
popgrowth	0.0008	−0.0046 ***
	(0.2595)	(−3.8329)
divorce	−0.0558 ***	−0.0044
	(−4.3354)	(−0.4287)
constant	−4.4814 ***	0.9637
	(−6.9243)	(0.2879)
Control variable	YES	YES
Regional effect	YES	YES
Time effect	NO	YES
N	374.0000	366.0000
R2_a	0.8704	0.9468
F	157.5355	363.5498

Note: (1) t-statistics are in parentheses. (2) *** and ** denote significant at the 1% and 5% levels, respectively. (3) Regression results for all control variables are not reported due to space limitations and are available from the authors upon request. (4) Regression results in subsequent tables include the corresponding control variable, region, and year fixed effects if not otherwise specified.

show that property taxes exhibit significant effects on commodity house sales prices to varying degrees in both least-squares and two-way fixed-effects regressions. Among these results, the effect of current property tax conversion values on housing prices is significantly positive in both regressions, but the effect of residential property tax measures is significantly negative in both regressions. In other words, the various property-related taxes currently implemented in China have contributed, to a certain extent, to the increase in commodity housing prices. However, academic studies speculate that we will reform the property taxation, especially by redesigning the tax system to include individual housing in the taxation scope and introducing a new property tax. It is expected that this will be used as a tool with which to combat speculation in real estate, and the more real estate owned, the larger the area or the greater the value of the home, the more taxable the taxpayer will be, and the heavier the burden will be. It is true that taxation will affect consumer prices, and the introduction of a new property tax will also curb the rise in commodity housing prices to a certain extent. These observations are fully consistent with the theory of tax effects, where the real-estate market is free to trade at a certain price and the demand for housing is elastic, and the increase or decrease in taxation on real estate will directly affect the increase or decrease in housing prices. Moreover, housing is a specific consumer product with the inherent special characteristics of real estate. Table 3 also shows that land revenue, i.e., land finance policy, indirectly affects the change in housing prices and, more importantly, the rise in the average social wage also obviously contributes to the rise in housing prices.

In order to verify the reliability of this result and further select a better model for the analysis, measurement screening of the validity of the variables was implemented, especially for the fixed- and random-effects selection, and the authors used the Hausman test for optimal screening of the model. The results were chi² (15) = 245.94, Prob > chi² = 0.0000, which means that the random-effects model was rejected and the fixed-effects model was selected. In the benchmark regression, the fixed-effects model can address the shortcomings of the mixed regression and random-effects models, i.e., it cannot deal with the omitted variable problem, but it needs to satisfy the irrelevance of the explanatory variables to the random disturbance term or requires the assumption of exogeneity of the explanatory variables. However, the main reason for the possible endogeneity of a property tax as the dependent variable in this paper is that there is a bidirectional causal relationship between a property tax and housing price. As discussed earlier, property taxes have an impact on housing prices, and in practice housing prices themselves may also have an impact on changes in property taxes. The stable change in supply and demand and price in the real-estate market is conducive to the stable change in the property tax source, which can not only improve the economic effect of property tax use, but can also play its functional role, which is conducive to the return of the healthy development of the real-estate market. At the same time, this can weaken the market’s financial attributes, which is more conducive to the realization of the policy of “houses are for living in, not for speculation”.

In summary, there may be a bidirectional causal relationship between both property taxes and housing prices, which can lead to endogeneity problems in the econometric model. For this reason, a more efficient treatment method needs to be chosen for econometric regression estimation. Based on the consideration that OLS and FE, which are traditional methods, can hardly solve the endogeneity problem, the authors next used instrumental variables 2-stage least-squares (IV-2SLS) for regression in order to obtain more precise research results.

4.2. Regression Results Based on the Causal Relationship between Property Taxes and Housing Prices

Usually, there are two conditions that need to be satisfied simultaneously when choosing the instrumental variables: correlation and exogeneity. Correlation requires the ability to explain the change in the original variable, while exogeneity requires that it is difficult to influence the dependent variable directly or indirectly, and this problem can only be better solved by the original variable. Throughout the development of China’s commercial housing market, China’s housing commercialization reform started in the 1980s and went through several housing reforms until 1998, when the welfare housing system was abolished before the liberalization reform of the commercial housing market was gradually realized. Unlike European and American countries, the house is the “root” culture of Chinese people, and the concept of having a house in order to have “roots” has a long history. In this cultural context, the demand to buy houses for marriages, sons, daughters, families, and even homes after divorce is endless, and some people jokingly say that “the future mother-in-law raises the price of housing in China”. All indications suggest that the consumption capacity of commercial housing in China is related to population, but the nature of people’s household registrations, marriages, births, and divorces cannot have a direct impact on real-estate development and sales. Therefore, the authors selected the urbanization rate, birth rate, natural population growth rate, and divorce rate as instrumental variables for the consumption capacity of commercial housing.

Then, using the above instrumental variables, a model was developed to test the relationship between property taxes and housing prices, regression was performed using the instrumental variable 2-stage least-squares (IV-2SLS) method, and the parameter results are presented in

Table 4. Impact of property taxes on housing prices (IV-2SLS estimation results).

	Commercial Housing	Housing
	(1)	(2)
proptax	0.0413 *** (4.8736)	0.0426 *** (4.4891)
rtax	−0.1948 *** (−6.5788)	−0.2056 *** (−6.3362)
comhs	0.3360 *** (2.7008)	0.3402 ** (2.4656)
landincome	0.4129 *** (5.9761)	0.4302***(5.6285)
avwage	0.3172 *** (3.5756)	0.2558 ** (2.5532)
pergdp	−0.0464 (−0.5353)	−0.0293 (−0.3012)
epu	−0.0143 (−0.4707)	0.0133 (0.3932)
Inflation	−0.0076 * (−1.7279)	−0.0088 * (−1.8419)
loanrate	0.1440 *** (6.9230)	0.1451 *** (6.2958)
respro	−0.2902 ** (−2.2193)	−0.2789 * (−1.9108)
cpotential	−0.3688 *** (−6.4891)	−0.4004 *** (−6.2692)
constant	0.166 (0.1697)	0.1958 (0.1802)
Stage I F	403.42	403.42
Stage II F	138.65	164.99
Stage I R2	0.9542	0.9542
Stage II R2	0.8757	0.8637
N	374.0000	374.0000

Note: (1) The table shows the results of the second-stage regression of instrumental variables, with z values in parentheses and R² values within groups. (2) ***, ** and * denote significant at the 1%, 5%, and 10% levels, respectively. This note also applies to the following tables.

. Column (1) shows the regression results for commodity housing prices as the dependent variable. In order to dig further into the relationship between property tax and residential commercial housing, we also conducted a regression using residential commercial housing prices as the dependent variable, and the detailed results are shown in Column (2), as well as a verification of the indicator substitution method test.

Firstly, the statistical implications of the first-stage regression F-values under both dependent variables indicate that there is no weak instrumental variable problem in either regression model. Secondly, the existing property-related taxes are positive and significant at the 1% level regardless of which housing price is used as the dependent variable, suggesting that current property taxes have a significant driving effect on housing price inflation, validating hypothesis 1. Thirdly, property taxes measured using effective tax rates, however, have a significant dampening effect on housing price inflation and are significant at the negative level, indicating that the property taxes to be introduced have a very robust effect on housing prices in the current economic context. Hypothesis 2 is verified by the fact that every 1 percentage point increase in the property tax will cause a 0.1948 percentage point drop in the price of commercial properties and a 0.2056 percentage point drop in the price of residential properties. Finally, in addition to the property tax, other main control variables also affect housing prices; the size of the commercial housing sales performance, the level of land revenue, changes in the average wage level, and adjustments in loan interest rates all positively affect housing prices, while the urban consumer price index, residential commercial housing sales performance, and total regional population have negative effects on housing prices.

It should be noted that GDP per capita, one of the measures of economic development, and EPU, an index of uncertainty in China’s economic policies, both have anomalous coefficients in the regression results. There are two possible reasons for this. The first is that the regression results may be affected by the limited sample size. The second is that there may be strong endogeneity due to a bidirectional causality between GDP/EPU and housing prices, but since they are not core independent variables in the above model, failure to treat them causes the regression results to behave abnormally. However, overall, the regression results using the IV-2SLS method confirm the hypothesis stated in the previous section that the currently implemented property-related taxes have contributed to the increase in housing prices and that the upcoming property tax will have a significant dampening effect on housing prices.

4.3. Robustness Tests

In order to enhance the validation of the robustness of the aforementioned measurement results and to reduce the effect of the omitted variables, various treatments were used in the econometric regressions, and the identification of the interactions between different dependent variables was used to better control for possible control variable endogeneity and selectivity bias.

Considering the impact on the issue of effective control of the heterogeneity of unobserved variables, further tests of endogeneity of instrumental variables, replacement of measures of dependent and independent variables, and moderating effects and tests were used to fully validate again to enhance the robustness and credibility of the findings.

(i)

Endogeneity test

The authors first performed estimation of the OLS effective estimator and 2SLS consistent estimator for the model in order to test whether there was an endogeneity problem in the above model and tested both estimation results using the Hausman test. In this test, the H0 hypothesis suggests that all explanatory variables are exogenous, but the test results were chi² (5) = 104.08 and Prob > chi2 = 0.0000, which show that the model rejected the original hypothesis, indicating that the variable cpotential is an endogenous explanatory variable. The authors also performed a weak instrumental variable test in order to prevent biased 2SLS estimation due to low correlation between the selected instrumental variables and the endogenous explanatory variables. The results show that the bias R² of cpotential is 0.1391 and the minimum eigenvalue statistic is 19.1736, which confirm rejection of the original hypothesis at the 10% confidence level, indicating that there is no weak instrumental variable problem. Finally, the authors also performed an overidentification test for 2SLS based on the Sargan statistic in order to test whether the instrumental variables are exogenous. The result of Sargan testing was chi (2) = 4.69778 (p = 0.0955), which indicates that the original hypothesis was accepted at the 5% significance level, i.e., the instrumental variables selected by the model are exogenous and meet the requirements of instrumental variables.

In the previous regression, the Hausman test was also applied to identify whether random or fixed effects were used, in order to determine whether the fixed-effects model was reasonable, and the authors found that neither choice affected the significance of the results. For this reason, the regression was conducted using the fixed-effects model. By testing the endogeneity problem as described above, the results indicate that the study findings are robust and credible.

(ii)

Robustness test by replacing the dependent variable

To test the robustness of the econometric model, the authors replaced the explanatory variable, the sales price of commercial properties, with the sales price of residential properties. As mentioned in the previous section, the most significant change in the impact of a property tax, which is a common concern in society, is the sales price of commercial housing; this is the most important aspect of commercial housing that is close to people’s lives. Therefore, the robustness test of Equation (5) was performed by regressing the model with the residential sales price as the dependent variable. The robustness test results are shown in

Table 5. Impact of property taxes on housing prices (robustness test).

	OLS	Two-Way Fixed Effect	Instrumental Variables
	(1)	(2)	(3)
proptax	0.0615 ***	0.0657 ***	0.0426 ***
	(8.7081)	(2.9382)	(4.4891)
rtax	−0.2238 ***	−0.0485 ***	−0.2056 ***
	(−8.7697)	(−2.8887)	(−6.3362)
Constant	0.4432	0.2308	0.1958
	(0.0527)	(0.0616)	(0.1802)
Control variable	Yes	Yes	Yes
N	374.0000	359.0000	374.0000
R2_a	0.9006	0.9463	0.8637
F	212.1389	353.2637	164.99

Note: Column (3) R² and F values are two-stage values, and their one-stage data are shown in column (2) of Table 4. *** denotes significant at the 1% level.

, in which Column (1) shows the OLS return result, Column (2) shows the two-way fixed-effect regression result, and Column (3) shows the instrument variable 2SLS regression result. The results of the robustness tests for the various methods indicate that current property-related taxes are positively correlated with residential sales prices, while residential housing taxes are negatively correlated with housing prices, and all are significant at the 1% level, again validating all of the previous hypotheses. These results still indicate that the previously constructed regression model is robust.

5. Conclusions

Housing prices have been a topic of concern worldwide, and their rise or fall has obvious causal effects on social and economic activities. This is particularly important to the Chinese government. If the real-estate market collapses, the Chinese financial system will also collapse as a result, and the Chinese economy will suffer serious setbacks and social instability, which will even affect the global economic development layout. However, if housing prices continue to rise steeply, a serious real-estate bubble will be triggered, and if it bursts, this will lead to greater financial system risks. Therefore, in order to stabilize housing prices, the Chinese government has been considering the opportunity to start collecting a property tax in recent years, attempting to use tax policy to regulate the real-estate market. The new property tax will be different from current property-related taxes, and many scholars infer that it is a new derivative tax. Although the property tax has not actually been levied, there has been dissemination of information regarding the government’s attempts to do so, so the levy will soon become a reality. What concerns all sectors of society most is what kind of impact the property tax will have on housing prices, and whether it will curb the rise in prices or fuel a new round of increases. These questions do not yet have unified theoretical answers. In view of this, based on the existing literature, the authors selected panel data from 31 provinces and regions in China during the period 2009–2020 with which to conduct an empirical study on the impact of a property tax on housing prices.

5.1. Conclusions

Firstly, although land control and real-estate development policies vary from country to country, the topic of housing prices is often discussed, and governments have tried to regulate housing prices by imposing property taxes with a view to regulating prices. The goal of regulation is mainly to curb inflated housing prices, curb speculation, stabilize housing prices, and avoid prices rising too fast and generating more real-estate economic bubbles. From our model regression results, it is clear that the current property-related taxes in China have no significant effect on public services and capitalization due to their fragmented distribution, and do not serve to curb housing prices, but instead contribute to the rise in housing prices. This empirical result is consistent with the hypothesis proposed in the paper, which makes it valid.

Secondly, although scholars have conducted many studies on property taxes in China, there are few studies that use effective tax rates to measure property tax data and, thus, infer the causal impact of property taxes and housing prices. Based on this, the authors can assess the effect of property taxes on housing prices after introducing predicted values of property taxes into the model. The results suggest that the introduction of a property tax will lead to a decline in housing prices, regardless of whether a house is purchased for residential or investment purposes. This finding is consistent with the expectation of most people from all walks of life that the introduction of a property tax will regulate housing prices, and verifies the hypothesis proposed in this paper. The levy of a property tax helps to curb housing prices, helps to squeeze the expansion of speculative demand for real estate, and helps to mitigate the harm caused by the real-estate bubble.

Thirdly, the limited nature of a single tax determines the contribution of its regulatory function. Infinitely exaggerating or reducing the role of taxes is pseudoscience, and property taxes are no exception. As the regression results of the previous model show, the effect of tax on housing prices is significant, but it should not be overstated. In addition to property taxes, a variety of factors, such as the average wage level of residents, consumer price levels, land finance, loan interest rates, demand for housing, the rate of change in population growth, and changes in the real-estate development environment, also affect housing price movements. Of course, there are also conventional factors, such as the location of housing, the quality of construction, and the surrounding residential and human environment, which also influence the relationship and produce regional differences in housing prices. In addition, housing prices are regulated by macrofactors such as changes in the economic environment and economic policy uncertainty.

Fourthly, the authors found that the capital situation of real-estate enterprises, business risks, and excessive penetration of real-estate agents into the market are also important factors affecting housing prices. In particular, the central government’s implementation of effective financial regulation of real-estate enterprises has effectively curbed the influx of excessive speculative capital into the real-estate development market, and local governments have regulated and interviewed real-estate agents. This has, to a certain extent, reduced the overheating of real-estate investment, avoided a serious oversupply in the market, and reduced the risk of a bursting real-estate bubble triggering a financial crisis.

Fifthly, China’s “root” culture—having a house means having roots—establishes a solid long-term demand for the real-estate market. The authors found that the births, movements, marriages, and divorces of the population will eventually affect the demand for housing, giving rise to rigid and flexible demand from some consumers, which, in turn, affects the supply, demand, and price changes in the real-estate market. Under the influence of this culture, the real-estate market is in greater need of government monitoring and timely regulation to ensure stable development. The authors also found that property taxes can interfere with the consumer psychology of homebuyers and influence their consumption decisions, which is beneficial to the progress of the “root” culture.

5.2. Regulation and Control Policies

Firstly, since the abolition of welfare housing in 1998, China’s real-estate market liberalization of commercial housing transactions gradually developed to the present system. Especially in recent years, housing prices have risen too fast, and long-term negative effects have emerged. If we rely on stimulating the real-estate industry to stimulate the economy, we will drink poison to quench thirst, and the crisis consequences will far exceed our estimates. International historical experience shows that a real-estate bubble burst will probably cause economic recession. China’s high housing prices under the bubble present a risk, and a subprime mortgage crisis may also exist; these certainly affect the long-term development of the economy. Because high housing prices squeeze out the consumption capacity of residents, consumers with immediate housing needs have to reduce other possible consumption expenditures, saving for the purchase of housing and for basic survival, which reduces people’s happiness and creates a vicious circle. In particular, for low-income people with housing needs and financial difficulties, the purchase of housing may exhaust lifetime savings, or require exhausting the savings of previous generations, so that the entire family’s consumption capacity and level will be greatly discounted. There are also some speculators who use various credit tools to hold multiple properties; most of the money becomes invested in real estate, and the total value of assets alone belongs to the rich, but there is often a lack of cash payment ability or liquidity. These factors affect consumers’ consumption outside of real estate, resulting in a lack of economic momentum or even inability to entice the consumer. Therefore, with the market supply and demand as the reference point, the government needs to implement the necessary intervention and introduce appropriate financial support policies to suppress the excessive liberalization of real-estate finance, to create a benign cycle of development of the real-estate market, and to build a long-term mechanism for steady and healthy development; this is clearly a good policy by which to reduce the financial attributes of real estate back to the residential attributes.

Secondly, current property-related taxes are scattered, and accelerating the legislation of a property tax is an important part of China’s fiscal and taxation system reform. The design of the property tax system should integrate the current property-related taxes and, on this basis, it is appropriate to set the basic tax guidelines for a broad tax base, low tax rate, and multiple preferences. Such a tax system can not only balance and adapt to taxpayers with different taxing abilities, but also meet the geographical characteristics of China’s vast territory, and also adapt to the tax burden levels of different provinces and regions as well as families with different income levels. Therefore, the design of preferential property tax policies should be treated differently, with full coverage of tax preferences for taxpayers at the level of residential demand, and no tax preferences for taxpayers at the level of investment demand; there could even be tax-plus levy intervention when necessary. Through these tax collection and management measures, the effect of a property tax in regulating income disparity and balancing wealth redistribution can be given full play. This tax effect can not only regulate income distribution, but also influence the consumer psychology and investment motive of homebuyers, which, in turn, will affect the housing price. Of course, if the property tax leads to an excessive drop in housing prices and an economic downturn, the property tax levy and its preferential mechanism should also be adjusted to return to a virtuous cycle.

Thirdly, the government should consider the characteristics of different groups when formulating property tax policies in the future and strengthen tax incentives. The property tax should be levied to perform its function, not just to suppress property prices, which requires that property tax legislation should maintain its tax-neutral principle. Therefore, the basis of a property tax needs to be updated in a timely manner when using the assessed value, and the nominal tax rate should be more integrated to avoid tax aggressiveness and reduce tax regression at the same time. Property tax collection and management should promote tax capitalization, which can improve the distribution of real-estate wealth and, more importantly, play the role of a property tax function to promote the stable, healthy, and sustainable development of the real-estate market.

Fourthly, the government’s next step in formulating housing price policy, in addition to the property tax, should take into account a variety of factors, such as the average wage level of residents, consumer price level, land business income, real-estate sales, loan interest rates, demand for housing population, rate of change in population growth, return on investment in real-estate development, and risk, and also focus on macroadjustment factors, such as the general economic environment trends and economic uncertainty. Consideration should be given to the different roles and interactions of these factors to formulate appropriate housing price policies according to the city. Otherwise, it is difficult to achieve the purpose of controlling the rise in housing prices simply by levying a property tax.

Fifthly, the government should consider the influence of real-estate agents when introducing a property tax and should introduce corresponding tax supervision methods for different ways of underwriting, selling, and distribution. The government needs to establish a real-estate market transaction information platform to provide consumers with information on real-estate development and sales, second-hand housing transactions, and consumer demand through government public services to solve the information asymmetry problem. Using the information symmetry platform of free-market transactions under government supervision can reduce the manipulation of market prices, reduce false propaganda, and even solve common social problems, such as multiple sales of one house, multiple owners of one house, rotten buildings, and developers running away.

Sixthly, the government should take the lead in cultural propaganda to guide consumers to establish a new cultural concept of housing “roots”. At the same time, the government should promote a deeper reform of real-estate property rights, establish a multilevel and diversified property rights system, adapt to different housing consumer groups, and solve the contradiction of various housing needs with uneven economic ability. In this way, we can remove the traditional cultural shackles that attach too much importance to the ownership of property rights, and we can form a variety of models in which property rights are shared, jointly owned, or enable coexisting or even mixing, so as to realize the return of housing without speculation and with rationality.

5.3. Limitations and Future Research Directions

This study still has some shortcomings, and future research can be further improved by examining the level of housing prices and regional economic development in each city in China. A series of studies have been conducted on the impact of housing prices and macroeconomics, but few studies have focused on economic fluctuations, housing price changes, and the impact of property taxes in each city. Due to the low availability of microdata, we inferred causality between housing prices and a property tax in terms of provinces, and the parameters obtained from the regression will be kept for comparative analysis when a larger sample is available later. In future explorations, the authors will fully consider the locational and cultural differences among different cities, and study the relationship between a property tax, regional economies, and regional housing prices from a more microperspective.