The collapse of communism in the early 1990s launched an era of political and economic reforms in Russia and throughout the former Soviet Union that introduced democracy and the free market economy to countries that previously had no experience with either of these concepts. In Russia privatization of land was one of the first items on the reform agenda, and by the end of 1992 the Russian Parliament had adopted the federal law On the Payment for Land. This law set normative land values differentiated by regions to be used for taxation, as well as a basis for land rent and purchase. At the time the country had no land market, so this was considered a very progressive measure. Lands that were previously held in public ownership were rapidly distributed to individuals, and by 1998 some 129 million hectares of land were privately held by some 43 million landowners. Introduction of private ownership rights in land also meant the introduction of the land tax, since owners or users of land plots became eligible to pay for their real property assets.

Economic reforms in Russia were accompanied by inflation that ran thousands of percent annually. To maintain revenue yields, local and regional authorities adjusted normative land values accordingly. As land market activity started to develop in the mid-1990s, some of these authorities used market price information to make land value adjustments. As a result land taxes became absolutely inconsistent with the economic situation, and tax amounts were not comparable for similar properties located in different jurisdictions.

By the late 1990s the land tax system had developed faults that required tax reform on a nationwide scale. The basic outline of the tax reform included the following features:

• The land tax will become a local tax.
• While floating tax rates will be established by local governments, the maximum possible tax rates will be fixed by federal legislation.
• The federal government will develop rules and procedures for mass valuation of land plots.
• The tax base will be the cadastral value of land plots.
• Land cadastre authorities will provide information on taxable objects and their taxable land values to tax and revenue authorities.

Reform of the land tax is seen as part of a wider property tax reform. The current property tax system in Russia includes a number of taxes: individual property tax; enterprise property tax; land tax; and real property tax. While the first three are operational, the fourth tax has been tested as an experiment since 1997 in two cities, Novgorod Veliky and Tver (Malme and Youngman 2001, Chapter 6). It is expected that when Russia is in a position to introduce the real property tax nationally, the first three taxes will be canceled.

In 1999 the Land Cadastre Service of Russia, a land administration authority of the federal government, was delegated the responsibility to develop mass valuation methods and to implement the country’s first mass valuation of all land. The government chose mass valuation, identifying the sales comparison, income and cost approaches as the basic valuation models that needed to be developed. Land is valued at its site value as if it were vacant.

Implementation of a mass valuation system has been constrained by the lack of reliable land market data, however. The housing market is the only developed market in Russia that can be characterized by a large number of sales transactions. These transactions are spread unevenly throughout the country, with large cities characterized by many transactions and high prices for apartments, whereas small towns and settlements have few examples of real estate sales. The national land market recorded some 5.5 million transactions annually, with only about 6 percent of them being actual buying and selling transactions. Official data from land registration authorities could not be used as a data source because transacting parties often conceal the true market price to avoid paying transfer taxes.

This lack of reliable market data has forced the developers of mass valuation models to identify other factors that may influence the land market. The model developed for valuation of urban land included some 90 layers of information that were geo-referenced to digital land cadastre maps of cities and towns. Apart from available market information, these data layers included features of physical infrastructure such as transport, public utilities, schools, stores and other structures. Environmental factors also are taken into consideration.

Mass valuation methods in Russia have identified 14 types of urban land use that can be assigned to each cadastral block. Thus, the model can set the tax base according to the current or highest and best land use. The actual tax base established for each land plot is calculated as the price of a square meter of land in a cadastral block multiplied by the area of the plot.

It took one year of development and model testing and two years of further work to complete the cadastral valuation of urban land throughout Russia. Actual valuation results suggest that the model works accurately with lands occupied by the housing sector. The correlation between actual market data and mass valuation results is between 0.6 and 0.7 on a scale of 0 to 1.0, with greater accuracy in areas where the land market is better developed.

Cadastral valuation of agricultural land is based on the income approach, since availability of agricultural land market information is extremely limited. Legislation allowing the sale of agricultural land became effective in early 2002. The data used to value agricultural land included information on soils and actual farm production figures over the last 30 years. Mass valuation of forested lands was also based on the income approach. Russian land law also identifies a special group of industrial lands located outside the city limits that includes industrial sites, roads, railroads, and energy and transport facilities. These lands proved to be a difficult subject for mass valuation because there are so many unique types of structures and objects on them; individual valuation is often applied to them instead.

Over the past four years, some 95 percent of Russia’s territory has been valued using mass valuation methodology. The Federal Land Cadastre Service continues to refine and improve its methods in preparation for the enactment of relevant legislation authorizing the introduction of a new value-based land tax. During this period, the Cadastre Service organized a Workshop on Mass Valuation Systems of Land (Real Estate) for Taxation Purposes, in Moscow in 2002, under the auspices of the United Nations Economic Commission for Europe. It also assembled a delegation for the Lincoln Institute’s course Introducing a Market Value-Based Mass Appraisal System for Taxation of Real Property, in Vilnius in 2003 (see related article).

Alexey L. Overchuk is deputy chief of the Federal Land Cadastre Service of Russia and deputy chairman of the United Nations Economic Commission for Europe (UNECE) Working Party for Land Administration.

Reference

Malme, Jane H. and Joan M. Youngman. 2001. The Development of Property Taxation in Economies in Transition: Case Studies from Central and Eastern Europe. Washington, DC: The World Bank. Available at http://www1.worldbank.org/wbiep/decentralization/library9/malme_propertytax.pdf

Housing is an important component of both a household’s net worth and aggregate national wealth or stock of residential capital. Aggregate residential wealth is the sum of the values of all housing units. In Brazil, residential structures represent about one-third of total net fixed capital, so their value is important for economic and social policy. This analysis asks: What variables determine the stock values of residential property? How do location and neighborhood conditions affect these values? What is the aggregate residential wealth in the Rio de Janeiro Metropolitan Region (Metro Rio)? What is its distribution among household income and housing value groups? In other words, what generates residential wealth? How much residential wealth is there? Who holds it? Where is it located? (Vetter, Beltrão, and Massena 2013.)

Methodology for Estimating Residential Wealth

To address these questions, we first calibrated a hedonic residential rent model with sample microdata from the 2010 population census conducted by the Brazilian Institute of Geography and Statistics (IBGE). The units of analysis are households living in private, permanent housing units in urban areas of Metro Rio. The total number of households in 2010 was 3.9 million, and our sample is 223,534 (5.7 percent). We used the 41,396 renters in the sample to calibrate our model and then estimated the rents for homeowners and the landlords of rent-free units. Finally, we transformed the actual and imputed rents into housing values by dividing them by the monthly discount rate of 0.75 percent (9.38 percent annual rate), as is standard practice for Brazilian residential wealth studies (Cruz and Morais 2000, Reiff and Barbosa 2005, and Tafner and Carvalho 2007).

The underlying assumption in these studies is that the hedonic prices of the characteristics in the model and the discount rate are similar for rental and nonrental units. These are strong but necessary assumptions for the application of the methodology with the existing census microdata. The sum of estimated housing values is our measure of residential wealth. The objective is to estimate the aggregate value of all housing units and their average values.

In calculating average housing prices for these groups, we do not control for housing size or other characteristics, as would be done for hedonic housing price indices. Using census microdata, we can also estimate the residential wealth by household income as well as for smaller spatial units within municipalities, such as neighborhoods or districts. Even though the sample of rental units is relatively large, sample size drops rapidly as rents and household incomes rise, and the variances are particularly high for the open group at the top end of the distribution. Because we do not have data on the value of mortgages, our measure is of gross rather than net residential wealth.

Using rents from the census or a household survey compares favorably with other commonly used methods for estimating residential wealth for the Brazilian national accounts and related studies (Garner 2004), such as asking homeowners to estimate the selling price or monthly rent of their homes, using the asking prices for home sales, or using the prices registered when recording the sale. Whereas renters know their monthly rent payment, the informants may have little understanding of current trends in housing prices, and the original asking price is often higher than the final sale price. In Rio de Janeiro, the municipal government uses its own estimates of the sale prices based on asking prices, rather than the value registered in calculating the real estate transfer tax, because buyers and sellers often register lower prices.

In our hedonic residential rent model, the dependent variable is a vector of residential rents, and the independent variables are matrices of the structural characteristics of the housing unit, access to employment, and neighborhood characteristics, including indicators of access to urban infrastructure and services. The variables used are for the household per se and also for the census area in which it is located. Figure 1 shows Metro Rio’s 336 census areas and the larger municipal boundaries grouped into six subregions based on indicators analyzed in this and previous studies (Lago 2010).

The indicator for access to employment measures the average commute time to work for residents in each of the census areas. Figure 2 (p. 16) shows that the average commute time increases with distance from the center, but not by as much as one might expect—partly due to increased traffic congestion in all areas and to the fact that Metro Rio is polycentric with many subordinate centers.

The indicators of the quality of neighborhood infrastructure and services include the household`s access to the public sewer and water systems, garbage collection, and block conditions (e.g., street paving and drainage). As these indicators are highly intercorrelated, the component scores from a principal components analysis serve as the independent variables in the hedonic model. Component 1 explains 46.6 percent of the variance and shows high positive loadings on adequate block conditions and infrastructure, and high negative loadings on inadequate block conditions (e.g., garbage in the street and open sewers), indicating which areas have a higher level of attractiveness or desirability (figure 3). Although the lowest scores are clearly concentrated in the outlying areas, the patterns of attractiveness vary considerably. As with commute times, the distribution pattern of the attractiveness scores reveals the complexity of Metro Rio’s spatial structure.

Our hedonic model explains 73 percent of the variance of residential rent. The key independent variables are statistically significant; neighborhood quality and access to employment explain nearly two-thirds of the variance, while the structural characteristics of the housing explain only about one-third of the variance. In other words, the bulk of housing value is the capitalized value of access to employment and to neighborhood infrastructure and services, all of which are determined in large part by public expenditures. Figure 4 (p. 18) shows the distribution of average estimated housing values for census areas in US$ determined by our methodology. (The average exchange rate for 2010 is US$1=R$1.76.) These values tend to be highest in areas affording relatively low commute times and good access to urban infrastructure and services.

Distribution of Residential Wealth

How much residential wealth is the property of homeowners versus the landlords of rental properties and rent-free units used by employers, family members, or others? Our estimate of Metro Rio’s aggregate residential wealth of both occupied and unoccupied units in 2010 is US$155.1 billion (94.2 percent of Metro Rio’s 2010 GDP of US$164.4 billion) and US$140.2 billion for occupied units only (84.2 percent of Metro Rio’s GDP). Among total occupied units, 74.8 percent of this residential wealth (about US$105 billion) belongs to owner-occupied units, and the rest belongs to landlords of rented and rent-free units. In the case of lower-income households, the landlords could be another lower-income family.

Table 1 shows that the percent of homeowners is quite similar for all household income groups. For example, homeowners occupy nearly three-quarters of the households in the lowest household income group (with fewer than two minimum salaries or an average annual income of only US$4,407). A key reason for these high homeownership levels is that those living in favelas, squatter settlements, or other types of informal housing can declare themselves homeowners, even if they do not legally own the land on which their home is located. The 2010 Census showed more than 520,000 households (more than 15 percent of the total private permanent urban households) living in these types of settlements in Metro Rio. Land ownership in these settlements is a complex legal question on which even lawyers may not agree, since the chances of removal (at least removal without compensation) are quite low, and those living on land without a legal title may be eligible for squatter’s rights after five years under Brazilian law.

Although 25.3 percent of total households earned less than two minimum salaries (US$ 6,960 per year), the homeowners in this group held only 15.3 percent of the aggregate residential wealth of all homeowners. By contrast, only 15.6 percent of households earned 10 or more minimum salaries (US$34,800 per year), but homeowners in this income group held 34.5 percent of the aggregate residential wealth. Nonetheless, lower income households have more residential wealth than one might expect, in part because they are often homeowners in informal settlements.

Figure 5 (p. 19) shows the Lorenz Curve for the distribution of aggregate residential wealth of homeowners by housing value groups. This distribution is quite unequal, because the nearly 23.7 percent who are not homeowners have no such wealth (as shown where the Lorenz curve runs along the bottom of the axis) and because those living in higher-priced housing have greater residential wealth.

Distribution of Residential Wealth by Subregions

The bulk of aggregate residential wealth is held by those living in the suburbs and periphery around Metro Rio, although the average value of their housing units is lower. Table 2 shows that those subregions (4 and 6) together represent 79 percent of Metro Rio’s total households (3.1 million) and 58.1 percent of aggregate residential wealth (US$80.9 billion). Subregion 2 (the older, higher-income neighborhoods along the bay and coast) holds only 6.3 percent of Metro Rio’s households (about 242,000) and 19.0 percent of its residential wealth.

The percentage of renters is highest in the large squatter settlements (subregion 5), at 28.6 percent, with an additional 2.7 percent of rent-free units. Homeownership rates are highest (80.4 percent) in the periphery (subregion 6), where many owners live on land for which they do not have full legal title, though these areas generally are not squatter settlements as defined by IBGE.

Spatial Distribution of Household Income

One result of the interplay of market forces that shape residential rent and housing prices is that the distribution of aggregate household income tends to mirror the distribution of aggregate residential wealth. In other words, there is a relatively high residential segregation by income groups, with lower-income families concentrated in the large squatter settlements and in the suburbs and periphery (subregions 4, 5, and 6). High spatial concentration of higher-income households generates higher aggregate income and demand in areas that support higher-level services—in turn making these areas more attractive to higher-income homebuyers and renters. Figure 6 (p. 20) shows that the average annual household incomes for the census areas in 2010 reflect to a large extent the distribution of average housing values (figure 4), commute times (figure 2), and neighborhood attractiveness (figure 3).

In 2010, the high-income Barra da Tijuca area (subregion 3) held only 2.1 percent of total households in Metro Rio but 8.1 percent of aggregate household income and 7.6 percent of aggregate residential wealth. By comparison, the four large squatter settlements of subregion 5 held 2.5 percent of total households but only 1.0 percent of aggregate household income and 1.4 percent of residential wealth. Nonetheless, the aggregate residential value in these four squatter settlements was nearly US$2 billion, and the average housing value was almost US$21,000. These results show a relatively high spatial concentration of both aggregate household income and residential wealth that is tempered slightly by the home-ownership rate in squatter settlements.

Implications for Methodology and Policy Decisions

The methodology used in this analysis provides interesting insights into the macroeconomic and social importance of residential wealth; the variables that generate it; its distribution among household tenure, income, and housing value groups; and its allocation among subregions ranging from high-income neighborhoods to squatter settlements. The strong assumptions required in using the methodology must be taken into account when interpreting the results. Data from property registries or other sources with more detailed information on unit size could eventually be used to complement this methodology.

Government services, investments, and regulatory actions can result in benefits (e.g., access to employment, urban services, and amenities) and costs (e.g., taxes, fees, and negative environmental impacts) that are capitalized into the value of housing in the affected neighborhoods. For homeowners, positive net benefits from government actions increase their residential wealth, because they are capitalized in the value of their housing. However, for renters and new homebuyers, these same government actions can cause rents and housing prices to rise along with the net benefits. Some households, especially the lower-income renters and homebuyers, may have to leave the benefited area, and other potential new owners may be unable to locate in the area. Thus, housing tenure is important in determining whether or not a household receives the net benefits of government investments and regulatory actions.

Capitalization of the net benefits of government actions would clearly be an issue for the more than 30 percent of households in the four large squatter settlements that are not homeowners, as well as for those entering the housing market. Although there are no reliable data on housing turnover, we know that the total number of urban households in Metro Rio increased more than 20 percent, by almost 657,000, between 2000 and 2010. This increment was 14 percent higher than the total number of households in the Municipality of Curitiba (the state capital of Paraná) in 2010 and well over twice the number in Washington, D.C. All these new households, plus all the renters (about one-fifth of total households) and homeowners wishing to move, would be subject to increased rents and housing prices generated by the net benefits of government actions.

These results demonstrate a need for policies to ensure that rising rents and housing prices do not exclude some households from areas where public services and infrastructure are being improved. For example, financial assistance for home purchases could be part of the improvement program. One way of financing the needed lower-income housing and investment programs would be to capture part of the value being generated by infrastructure investments from higher-income households. Capturing part of the value generated by urban investments could help finance additional housing subsidies for lower-income families, as well as added investment, thereby providing a kind of investment multiplier.

About the Authors

David M. Vetter (Ph.D. University of California) has worked for more than four decades on urban finance and economics issues in Latin America for Brazilian entities, at the World Bank and Dexia Credit Local, and also as a consultant.

Kaizô I. Beltrão (Ph.D. Princeton University) was the dean and a senior researcher at the National Statistics School (an entity of IBGE) and is now a full professor and senior researcher at the Fundação Getúlio Vargas.

Rosa M. R. Massena (Doctorate, Université de Bordeaux) was a senior researcher at the IBGE for 23 years and since then has worked as a consultant on social indicators programs for Habitat, the World Bank, UNDP, and other entities.

Resources

Cruz, Bruno. O. and Maria P. Morais. 2000. Demand for Housing and Urban Services in Brazil: A Hedonic Approach. Paper presented at the European Network for Housing Research Conference, Gavle, Sweden (June).

Garner, Thesia I. 2004. Incorporating the Value of Owner-Occupied Housing in Poverty Measurement. Prepared for the Workshop on Experimental Poverty Measures, Committee on National Statistics. Washington, D.C.: The National Academies.

Lago, Luciana C. 2010. Olhares Sobre a Metrópole do Rio de Janeiro: Economia, Sociedade e Território. Rio de Janeiro, Brazil: Observatório das Metrópoles, FASE, IPPUR/UFRJ.

Reiff, Luis. O. and Ana L. Barbosa. 2005. Housing Stock in Brazil: Estimation Based on a Hedonic Price Model. Paper No. 21. Basel, Switzerland: Bank for International Settlements.

Tafner, Paulo and Marcia Carvalho. 2007. Evolução da Distribuição Familiar da Riqueza Imobiliária no Brasil: 1995–2004. Revista de Economia 33(2) (Julho-Dezembro): 7–40.

Vetter, David M., Kaizô I. Beltrão, and Rosa R. Massena. 2013. The Determinants of Residential Wealth and Its Distribution in Space and Among Household Income Groups in the Rio de Janeiro Metropolitan Region: A Hedonic Analysis of the 2010 Census Data. Working Paper. Cambridge, MA: Lincoln Institute of Land Policy.