In this section, the sources, transformations and characteristics of data are described. The list of data with their sources are reported in Table 2. Below, we describe the data used in this study in more detail.
It is difficult to obtain overlapping time series for the variables under different base periods in Latin American countries over the long-run and Argentina is not an exception. It is typical that, once the base period is changed, the old time series (based on the previous base period) are discontinued and the new ones are not extended backward for a significant number of years. Frequently, a change in the base period usually reflects improvements in statistical procedures that began in Argentina during the 1960s when its statistical system started developing. This makes unclear whether the observed differences across base periods effectively reflect changes in the series or merely shows the peculiarities of statistical procedures. Regardless, we carefully describe the variables as shown in Table 2 and adopt the “second-best methodology” consisting in the simple “chaining” of the series as the only alternative available.
Dependent variable
As our dependent variable, we take the average monthly rent index of a room in the City of Buenos Aires for the period 1914–1934 published by the Departamento del Trabajo of the Ministerio del Interior through its statistics division. From 1934 to 1961, the numbers also come from a contemporary index considering the consumption of a \(4\times 4.5\) meters room by an unskilled worker’s family type (parents and two children under 14 years old) living in the City of Buenos Aires. We take the data from publications of the former Dirección Nacional de Estadística y Censos (DNEC, hereafter). This office also provides information for the 1961–1976 period and the variable turns out to be the rent of a house (excluding electricity) according to a survey on the living conditions of a working family (“familia obrera”) carried out in the capital of Argentina.
According to the official statistics that we follow, rent for 1977–1988 comes from the consumer price index and include housing expenses. The set of goods and services selected in the expenses are sanitary repairs, tiles, cement, bricks, wood, and paint. This group excludes other things (gas, the cost to refill a balloon of gas, kerosene, charcoal and electricity). The index considers the capital of Argentina. However, from March 1977 the index includes the capital of the country and 19 suburban communities. The information for the index comes from the Encuesta Permanente de Hogares (Permanent Household Survey) and the data are elaborated and published by the Instituto Nacional de Estadística y Censos (INDEC is the Spanish acronym for the National Institute for Statistic and Censuses). As for 1988–1999, the index measures the evolution of the monthly effectively rent paid by households, with expenses considered separately.
For 1999–2013, the survey of the rental prices is monthly and based on the division of the geographical area into work zones composed of the City of Buenos Aires and the Greater Buenos Aires.
Finally, for 2014–2017, data come from the Dirección General de Estadística y Censos of the City of Buenos Aires. The IPCBA (this is a Spanish acronym for Consumer Price Index of the City of Buenos Aires) contains the rent variable.
The time series of monthly rent coming from different sources are linked to obtain a series covering the period 1914–2017, which is the first attempt of this sort for Argentina. The individual rental price indices with different basis years and coverage are shown in Figure 2. This nominal rent is deflated using the consumer price index. The growth rates of the resulting time series are displayed in Figure 3.
In order to account for the methodological differences across these seven periods, in all regressions below, six dummies are introduced, for 1914–1934, 1935–1960, 1961–1976, 1977–1988, 1989–1998, and 1999–2013, denoted as \(D\_{\text{meth}}1, \ldots ,D\_{\text{meth}}6\).
Control variables
Interest rate. From 1914 to 2008, the series represents the interest rate for 30-days loans in domestic currency (peso) to first-line companies (prime rate). From 2009 on, it is the 30-days discount rate to promissory notes. Ferreres et al. (2005) provides information for the 1910–2004 period. The series is updated with information from the web page of the Banco Central de la República Argentina.
Gross Domestic Product As usual, the series is the sum of good and services produced by the Argentine economy during a year. Ferreres et al. (2005) covers 1910–2004, while the national accounts compiled from INDEC allow us to properly update the series.
Consumer Price Index The series is from Ferreres et al. (2005), who presents values up to 2004. However, to continue the series, we have to consider the government’s intervention in the Argentine Statistics Bureau (INDEC) from 2007 through 2015. During these years, the government started reporting official statistics that were systematically below the unofficial ones. We follow Cavallo and Bertolotto (2016) to update the annual series.
Population This variable indicates the projected population in thousands of persons. From 1910 to 2004 the data come from Ferreres et al. (2005), while the series up to 2017 are from Dirección General de Estadísticas y Censos de la Ciudad de Buenos Aires.
Building permits This variable broadly corresponds to the number of building permits, i.e., the administrative procedures through which the authorization for the construction of a building is requested. Each building permit generally corresponds to a work, so this variable largely reflects the number of buildings authorized. The source is the Revista Económica from the Banco de la Nación Argentina for the 1926–1934 period. A special request by the authors was made to the Dirección General de Estadística y Censos of the City of Buenos Aires for 1934–1943 data. From 1944 to now, data proceed from the building series of the national statistical office of Argentina through its different names (Dirección, Nacional del Servicio Estadístico, Dirección Nacional de Investigación Estadística y Censos, Dirección Nacional de Estadística y Censos, and INDEC). Unfortunately, to our knowledge, information about building permits is not available prior to 1926.
Demographics We use two demographic variables: the population growth and the growth of the number of marriages. This represents the demand side of the housing market. A large population represents a higher demand for the living space. Likewise, the number of marriages proxies the formation of the new households, each of which, at least in theory, requires a separate dwelling. It is expected that these variables should exert a positive impact on the housing rents.
Political orientation of the government The researchers both in political science and economics have used the left-right spectrum to explain political decision making, starting with Downs (1957). It can be expected that the leftist governments are more inclined to expand social policies, including rent control. The proxy used here is the orientation of the political party of the head of the government belongs. This variable is constructed by Brambor et al. (2017). It takes three values: \(-1\) for left, 0 for center, and 1 for right. Thus, the expected sign of the regressor is negative.
The evolution of control variables between 1910 and 2017 is shown in Fig.3. The real rent growth is quite volatile with several large peaks between 1960 and 1980. The real interest also varies wildly between − 70 and 20. It is very negative between 1960 and 1990 due to a large hyperinflation in that period. The population growth shows a secular decline with several cycles. Between 1910 and 2016, it dropped from 3.5 to 1% a year. The two indicators of the real GDP (Maddison Project Database and the indicator compiled by authors from different sources) have very similar dynamics, the compiled indicator showing slightly higher growth rates than that from the MPD. The building permits growth is also quite volatile, with the variation increasing toward the end of the sample. It is also a variable that starts in 1927, much later than all other time series. Finally, the left-right government index of Brambor et al. (2017) shows the fluctuations in the political orientation of the heads of government in Argentina. Most governments appear to belong to the right wing of the political spectrum. Only three times the leftists managed to gain control over the government: 1943–1955, 1966, and 2002–2012.
Regulation indices
This study focuses on the effects of governmental policies. Therefore, we need measures of their intensity. For this purpose, we use the restrictive rental market regulations indices elaborated by Kholodilin (2020b) and Weber (2017). These indices cover three types of regulations: rent control, tenure security, and housing rationing. All three indices vary between 0 and 1: the higher the index, the more intense the regulation. The indices are constructed for Argentina based on a thorough analysis of the corresponding legal acts. Table 4 summarizes all relevant laws underlying the rental market regulation indices utilized in this study. Figure 4 depicts the evolution of the three indices between 1910 and 2017, with shaded areas denoting both World Wars. For comparison purposes, it also shows the evolution of the indices for Latin America and the world.
Rent control index measures the intensity of restrictions imposed on the level of rent and its rate of increase. The index is computed as a simple average of six binary variables: 1) rent level control, if rents are set by some governmental body, court, arbitration council or similar and not by a free negotiation between tenants and landlords; 2) nominal rent freeze, if rent increases are prohibited; 3) real rent freeze, if rent increases are allowed but cannot exceed the growth of a cost of living index; 4) intertenancy decontrol, if a tenant change implies a deregulation of the formerly regulated dwelling; 5) other specific rent decontrol, if rent control is not applicable to certain types of dwellings (e.g., newly built or luxury ones); and 6) specific rent recontrol, if stricter rent control is applied to types specific households (e.g., low-income or having military as their member), areas (e.g., communities with tight housing market), or types of landlords (e.g., big ones). These binary indices take the value 1, if in particular year a corresponding restriction exists or exception is absent. Using the simple average implies equal weights of all the binary indices and, thus, their equal impact on the resulting composite index. Using different weights would be arbitrary, since it would be difficult to justify why certain binary indices should obtain higher weights. Moreover, even if some objective weighting rule could be found, it would be difficult to implement it due to the lack of data, when it is done on an international scale. In addition, the economists distinguish between first- and second-generation rent controls (Arnott, 1995). The first generation implies a rent freeze, when rents are fixed at some level. For instances, in Argentina rents were frozen three times: 1921–1924, 1943–1956, and 1965–1970, at the January 1, 1920 level, at the December 31, 1942 level, and at the previous contract level, respectively. Under the second-generation rent control, the rent level, as a rule, is not frozen; instead, the restrictions are imposed on the growth rate of rent, which is typically anchored to some measure reflecting the cost of living. In this way, lawmakers guarantee that the real rental revenues of the landlords are not eroded by inflation. In Argentina, in 1970, rent increases were capped by the rate of increase of the official index of living costs (índice de costo de vida). However, between 1987 and 2014, the rents were nominally frozen, for the government did not allow rent to be indexed by inflation in order to avoid an inflationary spiral. In terms of the index construction, the first-generation rent control implies that both rent control level and nominal and real rent freezes exist, while under the second-generation rent control, only real rent freeze exists.
The tenure security index reflects the degree of protection that tenants have from evictions by landlords. The main instruments of protection are 1) eviction protection during term or period; 2) eviction protection at the end of term or period; 3) imposition of a minimum duration of rental contracts; and 4) prohibition of short-term (less than one year) tenancies. Between 1921 and 1949, the first two tools were applied in Argentina: contracts could be automatically prolonged by tenants and landlords could only evict them, if they had justifiable reasons to do so. These reasons included: 1) non-payment of rent; 2) abusive use of the rented premises; 3) tenants initiating scandals (escándalo); 4) the owner needs the dwelling for himself and his family; or 5) the owner plans to rebuild the house, having low housing capacity, in order to create more dwellings, etc. In 1949–1957, the restriction on the minimum duration of rental contracts was added. In 1957, this requirement was abandoned. Finally, from 1976 on, the automatic prolongation of existing rental contracts was no longer provided to the tenants. However, during the term of those contracts, the tenants are still protected from eviction.
The housing rationing index measures the intensity of redistribution of the existing housing stock. In Argentina, between 1949 and 1965, three such policies were applied: 1) obligatory registration of vacant dwellings by landlords and subletting tenants within 15 days; 2) landlords are required to let their dwellings within 30 days; and 3) in the Federal Capital and National Territories, the authorities can requisition vacant dwellings.
The rental housing market regulation indices have their weaknesses, being a result of a tradeoff between the feasibility and the complexity of the real world. First, they are based on the formal laws and do not take into account their enforceability related to the effectiveness of the legal system and the degree of the legal literacy of the society. Second, these indices account for a limited number of relevant characteristics, skipping some other features. For example, they do not include the regulations concerning security deposits or subletting. Third, rental regulations typically apply to a specific segment of the housing market. If the size of this segments changes over time (for example, the rental market squeezes), the application sphere of regulations changes, too. However, due to the data limitations it is not always possible to take this into account.
All variables are tested for stationarity. The results of the augmented Dickey-Fuller unit-root tests are reported in Table 5. For the real interest rate (RIRate), the null hypothesis of unit-root (presence of random walk) can, in most cases, be rejected at conventional significance levels. Other variables become stationary after taking first differences. Only the growth rate of population (DLPop) appears to be non-stationary reflecting a secular decline in the speed of expansion of Argentina’s population. In addition, we tested our series for cointegration using the Engle-Granger test (Engle and Granger, 1987). The alternative hypothesis of the existence of a cointegrating relationship between the levels of variables could not be confirmed at 10% of significance. Therefore, we are going to use the growth rates in our regressions.