Metroeconomica. 2019;1–17. wileyonlinelibrary.com/journal/meca © 2019 John Wiley & Sons Ltd
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INTRODUCTION
A well‐established result of growth theory is that fiscal structures favouring non‐distortionary forms of taxation and productive public spending are growth‐enhancing (Barro, 1990). In this view, observers, scholars and international institutions, such as OECD (2010, 2012), the International Monetary Fund (Norregaard, 2013) and Eurostat (2014) have recently advocated property taxes as a
O R I G I N A L A R T I C L E
House prices and immovable property tax: Evidence
from OECD countries
Tommaso Oliviero
1|
Agnese Sacchi
2,3|
Annalisa Scognamiglio
1|
Alberto Zazzaro
1,41Dipartimento di Scienze Economiche e
Statistiche, CSEF, University of Naples Federico II, Naples, Italy
2Dipartimento di Economia e Diritto, La
Sapienza University, Rome, Italy
3GEN, Vigo, Spain 4MoFiR, Ancona, Italy Correspondence
Alberto Zazzaro, Dipartimento di Scienze Economiche e Statistiche, CSEF, University of Naples Federico II, Complesso Universitario di Monte Sant’Angelo, Cupa Nuova Cintia, 21, Naples 80126, Italy.
Email: [email protected]
Abstract
This paper studies the impact of changes in immovable property tax revenues on the growth rate of house prices by analysing a panel of 34 OECD countries over the period 1970–2014. Starting from the annual series of immovable property tax revenues, we isolate years of significant shifts in the property tax regime and study their impact on house prices. We find a strong negative relationship between in-creases in immovable property tax revenues and house prices. This relationship is robust to the inclusion of cyclical determinants of house prices, country and year fixed effects, and country‐specific linear trends. We also propose an in-strumental variable strategy based on countries’ legal ori-gins that confirms a statistically significant negative impact of such taxes in the short run.
J E L C L A S S I F I C A T I O N
E31, G12, H20, R32
K E Y W O R D S
non‐distortionary form of taxation for investment and labour choices, and therefore as an efficient and equitable fiscal remedy to public deficits (Presbitero, Sacchi, & Zazzaro, 2014).1
However, the empirical evidence on the impact of property taxes on GDP growth and other aggregate economic variables in developed countries is, at least, mixed. Using a sample of 22 OECD countries in the period 1970–1995, Kneller, Bleaney, and Gemmell (1999) find that increases in the total share of property taxes, social security contributions and taxes on payroll and manpower negatively affect GDP growth. Widmalm (2001) focuses on the contribution of property taxation alone, finding that the negative impact of such taxes on GDP growth rates is not robust to an extreme bounds analysis. Recently, Arnold et al. (2011) reexamined the link between the structure of tax revenues and GDP per capita in a sample of 21 OECD countries observed from 1970 to 2004. They show that a revenue‐neutral reallocation from in-come taxes to property taxes (especially recurrent taxes on immovable items) increases long run GDP per capita. Xing (2011, 2012), however, document that this result is not robust to the estimation methodology and that the contribution of property taxes to GDP in the long run is significantly positive only for a few countries, that is, Finland, Ireland and the United Kingdom. Similarly, Helms (1985), Ojede and Yamarik (2012) and Adkisson and Mohammed (2014) obtain ambiguous results for the United States.
One of the channels through which increases in property taxes may negatively or positively affect the real economy is the real estate market. If higher taxes on immovable property are capitalized into lower average house prices, the consequent negative wealth effects may adversely influence aggregate consumption, access to credit, investment (Adelino, Schoar, & Severino, 2015; Campbell & Cocco, 2007; Chaney, Sraer, & Thesmar, 2012) and, ultimately, GDP growth. The opposite happens when reductions in property taxes drive house prices upwards.
Determining the impact of property taxes on house prices has always been a topical and highly controversial issue (Mieszkowski & Zodrow, 1989; Netzer, 1966; Simon, 1943). According to the property tax capitalization hypothesis, a house has to be considered the same as any other asset whose price in equilibrium is equal to the present value of the after‐tax flow of rents from owning it (i.e., the rental price of housing services; see Kearl, 1979; Poterba, 1984; Poterba, Weil, & Shiller, 1991). Within this theoretical framework, an increase in the average property tax liabilities can be directly capitalized in house prices without affecting the rental price of housing services or it can be shifted to tenants resulting primarily in higher rental price of house services with little impact on house prices (Mieszkowski, 1972; Wilson, 2005; Zodrow & Mieszkowski, 1986). In addition, increases in property taxes are often accompanied by positive changes in the provision of local public services (e.g., schooling, health care, transportation, waste management, police ser-vices, etc.), and an increase in local property rents (Bai, Li, & Ouyang, 2014; Kang, Skidmore, & Reese, 2015; Oates, 1969; Rosen, 1982; Yinger, Bloom, Borsch‐Supan, & Ladd, 1988). Finally, higher property taxes reduce profits in the construction industry and therefore have a negative im-pact on the supply of new houses. If the price‐boosting effects of increased and improved availabil-ity of public services and lower housing supply dominate the negative effect of higher tax liabilities, an increase in property taxes can be associated with an increase in property values.2
1 Recurrent taxes on real estate property—according to the (Eurostat, 2014, p. 44) report on Taxation trends in the European Union—have attracted increasing attention from policy makers because in many countries where they are low they offer a
po-tential source of increasing revenue, while at the same time they are considered to be the least detrimental to economic growth given the immobility of the tax base.
2 Following Poterba et al. (1991), the equilibrium in the market for existing owner‐occupied houses requires that homeowners,
in their role as investors, earn the same return on housing investments as on other assets: Rh
Ph =(1 − 𝜃)(i + 𝜏p) + 𝛿 + 𝛼 + m − 𝜋e,
where Rh is the marginal value of the rental services per period on owner‐occupied homes, θ is the investor's marginal tax rate, i and τp are, respectively, the nominal interest rate, and the property tax rate as a share of house value (both deductible from
income taxes), δ is the depreciation rate on housing capital, α is the risk premium required on assets, m is the maintenance cost per unit value and πe is the investor's expected rate of nominal house price appreciation.
While there is an extensive empirical literature on the incidence of property taxes on property val-ues at the local level for single countries (especially the United States) based on micro data, surpris-ingly enough, cross‐country studies on the determinants of house prices have largely ignored the role of property taxes (Andrews, 2010; Andrews, Caldera Sanchez, & Johansson, 2011; Egert & Mihaljek, 2007; Girouard, Kennedy, van den Noord, & André, 2006; Tsatsaronis & Zhu, 2004). A recent excep-tion is Blöchliger, Égert, Alvarez, and Paciorek (2015) who estimate both a fiscal reacexcep-tion funcexcep-tion of property tax revenues to house prices and a house price reaction function to changes in property taxes for OECD countries. With regard to the latter, they find that house prices and property tax are negatively correlated and that a larger property tax base tends to reduce house price fluctuations.
This paper contributes to the above literature by introducing a new measure of immovable property tax revenues that isolates years/episodes of policy changes in the property tax system. Specifically, starting from the data on immovable property tax revenues in a sample of 34 OECD countries over the period 1970–2014, we distinguish episodes/years in which changes in immovable property tax revenues can be more confidently ascribed to fiscal reforms—for example, changes in tax rates—rather than to other factors, such as endogenous changes in the tax base due to changes in housing market values.
Our main finding is that, in years of increasing tax pressure on properties, aggregate house prices fall. In the years that we identify as fiscal reforms, the average annual growth rate of immovable property tax revenues is about 30%. We find that when a country experiences a significant increase in the growth rate of property tax revenues, the growth rate of house prices is 2.5–3.1 percentage points lower than in normal times. This result is robust to a wide range of specifications allowing for the inclusion of control variables for changes in fundamentals, country and time fixed effects and coun-try‐specific linear trends.
A possible concern for our analysis is that updates of the tax base (usually referring to cadastral values) may not result from a property tax reform, but simply be driven by automatic realignments to the market value of residential properties. For instance, in the United States the tax base automatically changes in relation to market values. To address this concern, as a robustness test, we exclude from the regression analysis countries that updated the cadastral values in the last decade. The fact that our results hold true when considering only countries where the tax base is invariant over time reassures our inter-pretation in terms of capitalization effects of the increase in the property tax rate on the market value of properties, while it makes the reverse effect of house prices on property tax base and revenues less likely.
In order to further rule out a spurious and biased interpretation of our results due to reverse cau-sality and other potential endogeneity issues between house prices and immovable property tax, we propose an instrumental variable strategy based on the legal origin of countries. Specifically, we in-strument the episodes/years where changes in the tax revenues are likely to result from policy changes with the interaction between the yearly change in GDP per capita and a dummy that indicates whether the country has a legal origin related to the French Commercial Code or Socialist/Communist Laws rather than to British, German or Scandinavian law. We discuss the hypothesis behind this strategy and show that IV estimates are largely consistent with our main findings.
The paper is structured as follows. Section 2 provides a description of the data and introduces the empirical strategy. Section 3 lays out the main empirical results. Section 4 shows the instrumental variable approach. Finally, Section 5 provides concluding remarks.
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DATA AND VARIABLES
The analysis draws on the house price index reported in the OECD housing prices database. This index is mainly based on the series of sales of newly‐built and existing residential dwellings for all
types of dwellings and is collected by the OECD, with the primary source being national central banks.3 The database contains country‐level indexes for the 34 OECD countries from 1970 (base year
2010).4 We use the real house price index, namely the nominal series deflated using the private
con-sumption deflator, drawn from the national account statistics.
Lacking statistics on property tax rates at the international level, we measure the tax burden on real estate by the property tax revenues.5 Following the standard international tax classifications, we
con-sider a specific component of the property tax which is the recurrent tax on the value or size of im-movable property. It mainly consists of regular annual levies on land or buildings—residential or commercial—and of taxes on property transactions. It represents two‐thirds of the whole property tax in the EU, and households constitute the largest group of taxpayers. Since most countries collect prop-erty taxes at the sub‐national level, to ensure cross‐country comparability, our empirical analysis is based on the series of total immovable property tax revenues consolidated at the general government level, as computed by the OECD Revenue Statistics. We acknowledge that changes in immovable property tax revenues may reflect changes in the fiscal pressure not only on residential properties, but also on other immovable properties. However, in the absence of comprehensive recent data on the division between residential, commercial and industrial property taxes (Blöchliger et al., 2017), we rely on the fact that residential property taxes account for the largest share of total property tax reve-nues (Blöchliger, 2015; Norregaard, 2013) and assume that large changes in immovable property tax revenues mostly result from taxation of residential properties.
In our sample, revenues from recurrent taxes on immovable property are, on average, about 1% of GDP. Figure 1 shows that the median value of immovable property tax over total revenues has been rising in recent years, with many countries increasing their property taxes mostly on immovable items. For instance, Italy introduced a new property tax system in 2012 that resulted in significantly higher revenues (Oliviero & Scognamiglio, 2017). Similarly, Hungary introduced a property tax in 2010 applied to real estate, water and airborne vehicles and high‐powered passenger cars, while Ireland introduced a new market‐value‐based property tax in 2012 as part of a broader fiscal reform (Blöchliger & Kim, 2016; Gayer & Mourre, 2012). Figure 1 also suggests that the upward trend in the median value of immovable property tax over total tax revenues started to reverse in 2014; the dynamics ob-served in the last decade may be ascribed to the fact that European governments that raised residential property taxes in the wake of the sovereign debt crisis lowered them afterwards.6
Table 1 reports the descriptive statistics of the variables that are used in the empirical analysis. The data set refers to an unbalanced panel of 34 OECD countries in the period 1970–2014. Δ log House
price indicates the yearly log change in real house prices; its mean is 0.015, meaning that the average
3 To the best of our knowledge, there are no international statistics providing harmonized data on residential property prices in
monetary units rather than indices.
4 Precisely, our sample includes: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland,
France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States. Data for some Eastern European countries are mostly available only for recent years.
5 The main problem is that property tax rates differ across jurisdictions in most countries, and often data are not even available
within the country. The only international statistics on the property tax rates of which we are aware can be drawn from the OECD fiscal decentralization database, and are collected from a survey of member countries for 2014. In addition, as no infor-mation on property values in monetary units is available (even with reference to a single base year), we cannot calculate the implicit effective property tax rates from the property tax revenues.
6 For example, in 2014 the Italian government abolished the property tax on primary residential properties that was introduced
annual growth rate of house prices is about 1.5%, and its standard deviation is 0.071. Δ log Imm tax
rev is the yearly log change of immovable property tax revenues in real terms; the average growth rate
of real immovable property tax revenues is about 3.7%, and its standard deviation is about 21%.
2.1
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Preliminary evidence
To provide preliminary evidence about the relationship between changes in immovable property tax and house prices we estimate the following model:
where i denotes the country and t the year.7 Our main coefficient of interest is β which provides an
estimate of the sensitivity of the growth rate of house prices to changes in the growth rate of immov-able property tax revenues, keeping constant the changes in all other explanatory variimmov-ables. ΔX denotes a set of control variables normally used in the literature to explain variations in house prices that may confound the estimated effect of changes in immovable property tax revenues. First, in order to meet budgetary needs and fiscal consolidation objectives, governments tend to raise immovable property taxes, especially during economic downturns. Therefore, the correlation between immovable property taxes and aggregate house prices may be driven by cyclical fluctuations in economic activity. To address this concern we include the variable Recession, which takes the value one if the annual (1)
Δlog House priceit= 𝛽 ⋅ Δ log Imm tax revit+ 𝛾 ⋅ ΔXit+ 𝜆i+ 𝜏t+ 𝜖it
7 Our specification can be derived by taking first differences of the following log‐log specification: log House pri-ceit = αi + β· log Imm tax revit + γXit + λi·t + ζt + ηit. After taking the first difference, we obtain Equation (1) where:
Δ log House priceit = log House priceit− log House priceit−1; Δ log Imm tax revit = log Imm tax revit− log Imm tax revit−1;
ΔXit = Xit−Xit−1; τt = ζt−ζt−1; εit = ηit−ηit−1. We finally estimate Equation (1) using the fixed effect method.
FIGURE 1 Median value of immovable property tax over total tax revenues. Source: OECD.
1
1.5
2
2.5
3
Immovable property tax
over total tax revenues (median)
1970 1980 1990 2000 2010
growth rate of real GDP per capita is negative, and zero otherwise. Second, we add the changes in the working age population (Δ log Working age population) to control for demographic developments as well as labour market factors, which normally influence the demand for housing.8 Third, we include
the log differences in total tax revenues (Δ log Total tax revenues) to control for the fact that changes in immovable property tax may occur at the same time as changes in other taxation items, which may in turn affect households’ disposable income and house prices.9 In this case we can interpret the
coef-ficient β as the estimated impact of changes in the tax structure towards taxation of immovable proper-ties on the growth rate of house prices, keeping constant the growth rate of the overall tax burden (Arnold et al., 2011; Ojede & Yamarik, 2012). Finally, we control for changes in the real long‐term interest rate (ΔLT real int rate), to take into account changes in monetary policy, which may affect house prices and be correlated with changes in property taxes (Egert & Mihaljek, 2007). Based on the user cost formula in Poterba et al. (1991) (see footnote 2), when the long‐term interest rate is high, the cost of homeownership is high too, and house prices are expected to decline (Himmelberg, Mayer, &
8 The importance of demography for housing demand and house prices has been empirically documented by Mankiw and Weil
(1989) and Poterba et al. (1991) for the United States. In particular, Mankiw and Weil (1989) find that the demand for housing sharply increases for individuals aged between 20 and 30 who enter the job market, and then smoothly declines after age 40.
9 The specifications that do not include the Δ log Total tax revenues variable (columns 1–3 in Tables 2 and 3) do not control for
movements of other government‐budget variables. Hence, the results from these specifications may be less useful from a policy perspective. However, the point estimates of the β coefficient do not statistically differ across specifications.
TABLE 1 Descriptive statistics
Variable Mean St. Dev. N. Obs.
Δ log House prices 0.015 0.071 973
Δ log Imm tax rev 0.037 0.209 1,117
Recession 0.230 0.421 1,205
Δ log Working age population 0.077 0.281 1,159
Δ log Dependency ratio −0.003 0.012 1,159
Δ log Total tax revenues 0.031 0.050 1,164
Δ log Tot tax rev ( net prop tax ) 0.031 0.051 1,164
Δ log Tot gov expenditure 0.025 0.048 699
Δ LT real int rate −0.037 1.712 864
Δ log Real gdp pc 0.019 0.035 1,165
Δ log Nominal gdp pc 0.078 0.088 1,171
Imm property tax revenues over gdp 0.010 0.009 1,205
Note. Data drawn from the OECD Statistics database. Δ log House price is the yearly log change in real house prices; Δ log Imm tax rev is the yearly log change in immovable property tax revenues; Recession is a dummy variable which takes value equal to 1 when the
yearly growth rate of real GDP per capita is negative; Δ log Working age population is the yearly log change in working age population (aged 15–64); Δ log Dependency ratio is the yearly difference of the log of the ratio between the population aged less than 15 or more than 64 and the working age population; Δ log Total tax revenues is the yearly log change in total government tax revenues; Δ log Tot
tax rev ( net prop tax ) is the yearly log change in total government tax revenues minus property tax revenues; Δ LT real int rate is the
yearly change of 10‐year government bonds nominal interest rate minus inflation (computed using the consumer price index), expressed in percentage; Δ log Total gov expenditure is the yearly log change in total government expenditure; Δ log Real gdp pc is the yearly log change in real GDP per capita; Δ log Nominal gdp pc is the yearly log change in nominal GDP per capita; Imm property tax revenues
over gdp is the ratio between Immovable property tax revenues and GDP. The series of the immovable property tax revenues, total tax
TABLE 2
Regression results from the estimation of Equation (1)
Δ log House price
Dependent variable: (1) (2) (3) (4) (5) Δ log
Imm tax rev
−0.020** (0.009) −0.017* (0.009) −0.019** (0.009) −0.019** (0.009) −0.018 (0.013) Recession −0.054*** (0.008) −0.053*** (0.008) −0.038*** (0.009) −0.031*** (0.008) Δ log
Working age population
0.041*** (0.012)
0.036*** (0.012)
0.050*** (0.014)
Δ
log
Total tax revenues
0.417*** (0.091)
0.395*** (0.107)
Δ
LT real int rate
0.000 (0.002) Observations 950 950 938 938 786 Adjusted R 2 0.192 0.267 0.277 0.324 0.362
Year and country fixed effects
Yes Yes Yes Yes Yes Note
. Standard errors clustered at country level in parentheses. *
p < 0.10, ** p < 0.05, *** p < 0.01. TABLE 3
Regression results from the estimation of Equation (2)
Δ log House price
Dependent variable: (1) (2) (3) (4) (5)
Increase in property tax revenues
−0.027** (0.010)
−0.027*** (0.010)
−0.028** (0.010)
−0.031*** (0.010)
−0.025** (0.011)
Decrease in property tax revenues
0.022 (0.021) 0.021 (0.016) 0.026* (0.015) 0.021* (0.012) 0.006 (0.022) Recession −0.055*** (0.008) −0.054*** (0.007) −0.038*** (0.009) −0.031*** (0.008) Δ log
Working age population
0.050*** (0.011)
0.043*** (0.010)
0.054*** (0.012)
Δ
log
Total tax revenues
0.449*** (0.093)
0.415*** (0.109)
Δ
LT real int rate
−0.000 (0.002) Observations 935 935 923 919 777 Adjusted R 2 0.196 0.275 0.290 0.345 0.371
Year and country fixed effects
Yes Yes Yes Yes Yes Note
. The average annual growth rate of immovable property tax revenues is 34% when the dummy
Increase in property tax revenues
takes the value 1, while it is −37% when the dummy
Decrease in
property tax revenues
takes the value 1. Standard errors clustered at country level in parentheses. *
p < 0.10, ** p < 0.05, *** p < 0.01.
Sinai, 2005). In addition, when monetary policy is contractionary (and interest rates high) households’ access to credit is limited, with detrimental effects on housing demand and prices (Kuttner, 2014).
Equation (1) includes country fixed effects (λi), in order to control for country‐specific
time‐invari-ant unobserved factors which can affect the average growth rate of house prices, and year fixed effects (τt) to control for aggregate shocks to the housing market common to all countries, for example, the
global financial crisis.
We estimate the model in first differences as the house price series are usually not stationary in levels (Egert & Mihaljek, 2007) and they have a non‐stochastic trend. The results of the panel unit root tests by Im, Pesaran, and Shin (2003), allowing for cross‐country heterogeneity, indicate that the se-ries are integrated of order one and stationary in first differences. Using first differences also ensures greater comparability of the dependent variable across countries: while cross‐country differences in the house price index are difficult to interpret economically, annual growth rates of house prices pro-vide a more uniform metric.
Regression results are reported in Table 2. Column (1) reports the estimated coefficient on the growth rate of immovable property tax for a parsimonious specification that only includes country and year fixed‐effects. The coefficient on Δ log Imm tax rev is negative and statistically significant which is consistent with the capitalization hypothesis in a cross‐country analysis. More precisely, an increase in 1 percentage point in the growth rate of immovable property tax revenues is associated with a decrease in the growth rate of house prices of about 0.02 percentage points.
Columns (2)–(5) show the estimated coefficients from specifications that include the additional control variables described above. As expected, increases in the working‐age population are posi-tively associated with increases in housing demand and prices. The dummy Recession is negaposi-tively correlated with the growth rate of house prices. The coefficient of ΔLT real int rate is not statistically significant, while an increase in the total tax revenues, probably capturing a positive economic‐cycle effect, is accompanied by an increase in aggregate house prices. Note that in column (5) coefficients are less precisely estimated. This loss in precision is likely due to the decrease in the total number of observations driven by the inclusion of changes in the interest rate as additional control variable.
Consistent with Goodhart and Hofmann (2002) and Kuttner (2014), we do not detect a significant relationship between the growth rate of house prices and the change in long‐term interest rates. This may be due to two (not mutually exclusive) reasons: (a) monetary policy‐induced increases in interest rates may be leaning against house prices (Adam & Woodford, 2018), thus introducing a positive bias in the coefficient; (b) there may be anticipation effects that bias the coefficient towards zero (to the extent that the change in the interest rate is anticipated it should be already reflected in house prices).
Although the magnitude and statistical significance of the estimated coefficient of interest are sta-ble across all specifications, there are two main concerns that affect the economic interpretation of the results reported in Table 2. The first is a measurement issue: yearly changes in real immovable prop-erty tax revenues are not necessarily related to policies that increase the tax pressure on immovable properties. Small changes over time may in fact be related to changes in the tax base possibly driven by country‐specific macroeconomic trends and only spuriously correlated with aggregate house prices. For this reason, in the next section, we present an empirical methodology that aims to identify policy‐ driven shifts in immovable property revenues.
A second issue relates to a pure endogeneity concern: policy‐driven changes in immovable prop-erty tax revenues can be ascribed to unobserved time‐varying factors at country level, which are not orthogonal to the market value of real estate properties. We present an instrumental variable approach that addresses this concern and allows us to confidently assess the presence of a negative impact of changes in immovable property tax revenues on changes in house prices.
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THE ROLE OF FISCAL REFORMS
As preliminary evidence of the impact of changes in immovable property tax regimes on house prices, we introduced a model which exploits yearly changes in tax revenues. The use of changes in tax revenues as a measure of changes in the tax pressure helps overcome the issue of identifying and measuring changes in the effective tax rates which are difficult to observe, especially in a cross‐coun-try framework. Differences in law, tax exemption regimes and fiscal federalism, make it difficult to identify a homogeneous measure of effective tax rates. However, yearly changes in property tax revenues do not necessarily capture tax policy changes. As highlighted above, small yearly changes in tax revenues can reflect marginal changes in the tax base which prove spuriously correlated with the market values of immovable properties.
In order to provide more compelling evidence of the impact of immovable property taxes on house prices, we need to isolate the years/episodes when the increase in immovable property tax revenues was arguably related to fiscal reforms rather than to changes in the tax base. Borrowing a methodology that is used in the literature to identify boom and bust cycles (bubbles) in asset prices, we suggest to identify policy shifts in the property tax regime (possibly due to changes in property tax rates or up-dates of the tax base—i.e., cadastral values) with the years when a substantial increase or decrease in the tax burden on immovable properties occurred.10
Specifically, following Bordo and Jeanne (2002) we define years of substantial increase (boom) and decrease (bust) in the property tax by comparing the 3‐year moving average of the annual growth rate of immovable property tax revenues with the long‐run historical average and standard deviation. Let gi,t = log(Ti,t) − log(Ti,t−1) be the annual growth rate of immovable property tax revenues in
coun-try i and time t, and mai,t = (gi,t + gi,t−1 + gi,t−2)/3 the 3‐year moving average of g. For each country
we consider year t to be a boom if mai,t > gi + δvi, where gi and vi are respectively the average and the
standard deviation of the annual growth rate of the immovable property tax revenues in country i, and
δ is a parameter which needs to be calibrated. Conversely, we consider a year t to be the (negative)
peak of a bust if mai,t < −gi−δvi. We build a dummy variable (Increase in tax) for each country, which
takes the value one in the year in which the 3‐year moving average of the annual change in immov-able property revenues exceeds the country‐specific threshold and, symmetrically, we define another dummy variable (Decrease in tax) which takes the value 1 when the 3‐year moving average is lower than the negative value of the same threshold.
In our preferred definition, parameter δ is set equal to 1.1. This parametrization ensures that the dummy variable Increase in tax takes the value one during some documented episodes of increase in property tax rates, as was the case of Finland in the early 1990s and Italy in 2012.11
In the regression analysis, we replace the variable Δ log Imm tax rev with the two dummy variables
Increase in tax and Decrease in tax while leaving the control variables unchanged, and estimate the
following model:
10 Given that we cannot fully exclude the concern that our measurement strategy also includes updates of the cadastral values
that are linked to changes in the market value of housing unrelated to tax policy interventions, in a robustness exercise we provide evidence that this concern is not validated in our sample and hence does not influence our main results.
11 However, results are robust to different definitions of each country‐specific threshold, which are obtained by giving a higher/
lower value to the parameter δ. Estimates obtained with different thresholds, not reported for brevity, are available upon request.
(2)
Δlog House priceit= 𝛽1⋅ Increase in taxit+ 𝛽2⋅ Decrease in taxit+ +𝛾 ⋅ Xit+ 𝜆i+ 𝜏t+ 𝜖it
where β1 and β2 are the main coefficients of interest, measuring the sensitivity of house prices to
immovable property tax reforms that significantly increase or decrease the burden of the tax.
The estimated coefficients, reported in Table 3, show that increases (decreases) in immovable property tax revenues are related to significant decreases (increases) in house price growth rates. However, while the impact of episodes of property tax increases is statistically different from zero in all specifications, the coefficients of decrease in property tax revenues are marginally significant only in columns (4) and (5). This asymmetry is possibly related to the different frequency of episodes of decrease with respect to episodes of increase in property tax revenues.12 The effect of an increase in
property taxes is not only statistically significant and robust, but also economically sizeable. When the dummy variable Increase in property tax revenues takes the value one, the Δ log House price is be-tween 2.5 and 3.1 percentage points lower than in normal times (both Decrease and Increase take the value zero). Note that the average growth rate of immovable property tax revenues when the dummy variable Increase in property tax revenues takes value equal to 1 is about 34%. To determine the im-pact of a 1 percentage point increase in the growth rate of immovable tax revenues conditional on an episode of fiscal reform, we divide our estimates by 34. Hence, conditional on episodes of fiscal re-forms, a 1 percentage point increase in the growth rate of property tax revenues induces a reduction of the growth rate of house prices between 0.07 and 0.09 percentage points.
Columns (2)–(5) of Table 3 show that results are very stable across different specifications. The coefficients of the control variables are qualitatively similar to those shown in Table 2. Unlike what is shown in column (5) of Table 2, the coefficient of interest is also significant when including Δ LT real
int rate as additional control.13
3.1
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Robustness
In this section we show that the results obtained by estimating model (2) are robust to a number of checks.
3.1.1
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Including country‐specific linear trends
As a first robustness check, we estimate a version of Equation (2) that includes country‐specific linear trends. Note that our baseline model allows for cross‐country differences in the average growth rate of house prices. By including also country‐specific linear trends, we are effectively taking into account also the possibility that countries differ in terms of the linear evolution of the growth rate of house prices over time. The rationale behind this analysis is to show that our main results are not driven by cross‐country differences in the dynamics of housing markets. Our results, reported in Table 4 (col-umn 1), confirm the findings shown in Table 3, thus excluding that they are driven by cross‐country differences in the dynamics of housing markets.
3.1.2
|
Accounting for mean reversion of house prices
Given the possible mean reversion behaviour of the house price series, we estimate a version of Equation (2) that includes as additional regressor the lag of the real house price index. The results,
12 In our sample, episodes of Increase occur with a relative frequency equal to 4.6%, while episodes of Decrease amount to 2.2%.
This is consistent with the positive trend of immovable property tax revenues shown in Figure 1.
13 In unreported regressions, we replicate Table 3 using the growth rate of real GDP per capita in place of the dummy Recession.
TABLE 4
Regression results for robustness checks
Δ log House price
Dependent variable: (1) (2) (3) (4) (5) (6)
Increase in property tax revenues
−0.036*** (0.010) −0.027*** (0.010) −0.028*** (0.009) −0.025** (0.011) −0.031*** (0.010) −0.030*** (0.010)
Decrease in property tax revenues
−0.012 (0.016) 0.027** (0.012) 0.010 (0.009) −0.024 (0.017) 0.019 (0.013) 0.021 (0.012) Recession −0.044*** (0.010) −0.035*** (0.008) −0.036*** (0.010) −0.035*** (0.008) −0.037*** (0.009) −0.038*** (0.009) Δ log Working age population 0.029** (0.013) 0.047*** (0.010) 0.044*** (0.011) 0.059*** (0.021) 0.043*** (0.010) Δ log Total tax revenues 0.480*** (0.102) 0.444*** (0.090) 0.529*** (0.067) 0.407*** (0.121) 0.446*** (0.093) L.log
real house prices
−0.041*** (0.011) Δ log Tot gov expenditure 0.127** (0.058) Δ log Dependency ratio −0.979*** (0.279) Δ log
Tot tax rev
(net prop tax ) 0.429*** (0.090) Observations 919 919 687 591 919 919 Adjusted R 2 0.278 0.358 0.370 0.440 0.345 0.344
Year and country fixed effects
Yes Yes Yes Yes Yes Yes
Country specific linear trend
Yes No No No No No Note
. Standard errors clustered at country level in parentheses. *
p < 0.10, ** p < 0.05, *** p < 0.01.
reported in column (2), show that the inclusion of this additional variable leaves the estimates of β1
and β2 virtually unchanged.
3.1.3
|
Excluding countries with frequent updates of the tax base
Although in many OECD countries the cadastral values have not been updated for years or even dec-ades, as documented by Blöchliger (2015), we cannot a priori exclude that our measures of increases and decreases of property tax revenues include updates of cadastral values linked to changes in the market value of housing.14 Indeed, in some countries, the assessment and evaluation of the tax base
occurs regularly and aims to keep cadastral values broadly aligned with market values. While this mechanism may generate a spurious correlation between house prices and property tax revenues, in our opinion, the bias of the estimated coefficients would likely be positive: if the tax base were to adjust with market values, an increase in house prices would induce a positive update of the tax base, in turn translating into higher property tax revenues (for any level of the property tax rate). Realignments of the tax base to market values would thus generate a mechanical positive correlation between changes in house prices and changes in immovable property tax revenues.
To show that our estimates are not affected by realignments of the tax base to market values, we rely on information provided by the OECD fiscal decentralization database and exclude from our analysis countries where a re‐valuation of the tax base has occurred in the last decade.15 Results,
re-ported in column (3), show that the sign and magnitude of the point estimates of β1 and β2 are in line
with the main results in Table 3.
3.1.4
|
Accounting for public expenditures
As stated above, the negative effect of property taxes on property values may be partially offset by an increase in the provision of local public goods funded by the property tax revenues. Ideally, one would control for different components of local public spending that are predicted to have positive ef-fects on house prices, like local spending in schools (Bai et al., 2014; Kang et al., 2015; Oates, 1969). Unfortunately, such detailed data are not available from the start of our sample. Therefore, to test whether our estimates of interest are affected by such countervailing effects, we expand our baseline model by including, as additional control, the yearly change in government expenditures in real terms. Column (4) of Table 4 shows that the coefficient of the growth rate of public expenditure is, as ex-pected, positive and significant, while the coefficient of the dummy Increase in property tax revenues is slightly smaller and less precisely estimated with respect to the baseline model in Table 3. While this result confirms the reliability of our estimates, we do not include the government expenditures in the baseline specification as this variable is not available for our entire sample: when including it among the controls the number of observations drops from 919 to 591.
3.1.5
|
Accounting for demography
We further provide a test for the robustness of our results to a different control for demographic fac-tors possibly affecting house prices. In the baseline model we control for the growth rate of working
14 Our measure of increase/decrease in immovable property tax revenues may isolate episodes of policy reforms that impact on
the tax rate as well as episodes of increase in the tax base which can be correlated with the market value of housing.
15 In particular we exclude the following countries: Australia, Denmark, Estonia, Ireland, Japan, Mexico, Switzerland and
age population to capture possible changes in demand for housing driven by changes in the population aged between 15 and 64, which primarily drives housing demand (Mankiw & Weil, 1989). However, as highlighted by Takáts (2012), ageing itself may negatively affect house prices. As the relative size of the old compared to those of working age increases, house prices may decrease, since working age people drive the housing demand and ageing mechanically leads to increases in supply. Hence, in column (5) of Table 4, we replace the growth rate of working age population with the yearly change in the dependency ratio. The coefficients of interest are unaffected by this change of control variable. Interestingly, the coefficient of the change in the dependency ratio is negative and significant, in line with the prediction of Takáts (2012).
3.1.6
|
Netting overall tax burden from property tax revenues
As a last robustness check, we repeat our baseline analysis by considering as control variable the yearly change in total tax revenues net of property tax revenues instead of the yearly change in total tax revenues (column (6) in Table 4). This change in the definition of the explanatory variable allows an alternative interpretation of our coefficients of interest relative to the baseline specification. In this version, indeed, our estimated coefficients capture the impact of increases and decreases in property tax revenues that do not necessarily entail shifts in the composition of total tax revenues towards the property tax component. We find that our baseline results are practically unaltered.
4
|
INSTRUMENTAL VARIABLE APPROACH
In order to address potential concerns regarding the endogeneity of property tax changes to changes in house prices, as a final test, we propose an instrumental variable approach that exploits cross‐coun-try variation in legal origins, and the fact that civil‐law countries differ from common‐law countries in terms of many economic and financial outcomes (Glaeser & Shleifer, 2002; La Porta, Lopez‐de Silanes, & Shleifer, 2008).
We divide our sample into two groups: (a) countries with a French or Socialist legal origin; (b) countries with a English, German and Scandinavian legal origin. We verify in the data that, when a negative (positive) shock to nominal GDP occurs, the first group of countries reacts by increasing (decreasing) the taxation on fixed capital and real estate properties (first stage analysis). We use this stylised fact as a source of variation in property taxes not driven by the dynamics of house prices, to address possible reverse causality issues that may affect the estimated relationship between property taxes and house prices shown in the previous subsections.
To implement our strategy, we construct a variable which takes the value of 1 for country‐year ob-servations when the variable Increase in tax is equal to 1, the value of ‐1 for country‐year obob-servations when the variable Decrease in tax is equal to 1, and the value of 0 otherwise. We name this variable
Boom/Bust in Tax.
We instrument this variable with the interaction between the yearly change in nominal GDP per capita and a dummy variable that takes the value one if the country belongs to the first legal origin group described above, and zero if it belongs to the second legal origin group.
Results from the first stage regression, reported in column (1) of Panel A of Table 5, show that there is a highly significant correlation between the instrument and the Boom/Bust in Tax variable, once controlling for the variable Recession. The F‐statistic from the first stage provides a value which is significantly above the rule‐of‐thumb threshold: it is equal to 16.53. Column (1) of Panel B reports
the 2SLS estimate of the impact of immovable property tax changes on the growth rate of house prices: the estimated coefficient is negative and significant.
In columns (2) and (3) we show that the 2SLS estimate is robust to the inclusion of the variables Δ log Working age population and Δ log Total tax revenues.16 While acknowledging the limits of the
instrumental variable approach in a cross‐country setting,17 this estimate provides an additional piece
of evidence that, coupled with the other robustness tests discussed above, confirms the soundness of our main results and ensures that they are robust to different specifications and estimation approaches.
5
|
CONCLUDING REMARKS
While immovable property tax revenues are low relative to total tax revenues and GDP in most devel-oped and developing countries (Almy, 2014), they have been recently advocated by governments and scholars as a non‐distortionary, growth‐enhancing form of taxation to be strengthened (Arnold et al., 2011; Norregaard, 2013). However, to the extent that lower house prices are associated with negative wealth effects on consumption, access to credit and investment, an increase in immovable property taxes may have adverse effects on the aggregate economic activity in the short run. Given its role in the current policy debate, and given the central role of the housing market for the macroeconomic
16 The F‐statistic is equal to 17.52 and 18.24 in column (2) and column (3), respectively.
17 The main concern is a possible violation of the exclusion restriction: countries characterized by different legal origins may
respond differently to GDP shocks not only in terms of property taxation, but also in terms of other factors than in turn may affect house prices.
TABLE 5 Estimation results from instrumental variable strategy
Panel A: first stage
Boom/Bust inproperty tax
Dependent variable: (1) (2) (3)
Legal origin×Δ log Nominal gdp
pc −1.612*** (0.396) −1.669*** (0.400) −1.763*** (0.413)
Recession −0.024 (0.024) −0.021 (0.024) −0.011 (0.024)
Δ log Working age population 0.095** (0.045) 0.090** (0.045)
Δ log Total tax revenues 0.329* (0.199)
Panel B: IV estimate
Δ log House price
Dependent variable: (1) (2) (3)
Boom/Bust in property tax −0.184*** (0.066) −0.170*** (0.063) −0.095* (0.054)
Recession −0.053*** (0.006) −0.052*** (0.006) −0.036*** (0.006) Δ log Working age population 0.063*** (0.013) 0.049*** (0.011)
Δ log Total tax revenues 0.428***
Observations 917 906 902
Year and country fixed effects Yes Yes Yes
scenario, it is important to study the possible effects from a shift towards this type of tax on the ag-gregate housing market.
In this study, we analysed the impact of changes in property tax on house prices in OECD coun-tries in the period between 1970 and 2014. Due to limited data availability at the international level, we considered property tax revenues and house price indexes rather than effective tax rates and house prices in monetary units. This introduces problems of measurement and endogeneity into the analysis that we address in the following way. First, we propose a new measure of property tax revenue in-creases/decreases that aims to isolate episodes of policy changes in the property tax system. Second, we develop an IV strategy, instrumenting episodes of policy‐driven changes in the property tax reve-nues with the interaction between the legal origin of the civil code of the country and yearly change in GDP per capita.
With these potential limitations in mind, in our empirical analysis, we provide evidence that strengthening the contribution of immovable property tax on the overall tax revenues has a nega-tive short‐run impact on house prices. However, in order to have a comprehensive assessment of the overall macroeconomic impact of changes in immovable property tax, it is necessary to evaluate their long‐term impact on the stability of the housing market and households’ demand (Mian & Sufi, 2018; Mian, Sufi, & Verner, 2017). In a longer‐term perspective the negative impact of immovable property taxes on house price dynamics can be counterbalanced by a greater stability of real estate markets, lower risks of speculative bubbles and household debt overhang. We leave these issues to future research.
ACKNOWLEDGEMENTS
We are grateful to two anonymous referees for constructive and extremely useful comments.
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How to cite this article: Oliviero T, Sacchi A, Scognamiglio A, Zazzaro A. House prices and
immovable property tax: Evidence from OECD countries. Metroeconomica. 2019;00:1–17.
