Collective Management of Residential Housing in Russia: The Importance of Being Social

In our empirical strategy we measure efficiency of Russian HOAs by using standard tools of productivity analysis, whereby an HOA is treated as a multiple-output production unit generating a stream of services for its members and using revenues as an input. Next, we relate the observed efficiency variations to exogenous factors representing tangible and intangible assets of HOAs as well as to institutional characteristics. The tangible assets are apartment buildings where HOA operate, characterized by their size and conditions. The main intangible asset is the capacity for collective action and self-organization among the tenants, reflecting social capital in their community.

Data for the study was collected by a survey of 82 HOAs conducted in the fall of 2008; of those HOA, 40 were based in Russia’s capital city of Moscow, and the rest in the major industrial city of Perm. The sample was balanced in apartment market values; building age; and the year HOA was formed. Random selection of buildings eliminates a censorship bias in our sample. In each HOA, the chairperson and nine other randomly selected tenants (apartment owners) were interviewed.

Questions of the survey were organized in the following categories:

1. 
   performance assessment: overall satisfaction with HOA performance and satisfaction with main services of HOA: common facilities maintenance; plumbing; electrical work; upkeep of the backyard; and waste removal;
   
2. 
   maintenance fees;
   
3. 
   governance: board’s accountability and quality of representation of tenants; and completeness and regularity of information disclosure;
   
4. 
   social cohesion of tenants: mutual help and support; interaction and socializing in everyday life; socio-economic inequality; and volunteering;
   
5. 
   participation in decision-making: attendance of general meetings; involvement in meeting deliberations; attention to other tenants’ opinions and concerns; ability to compromise and reach a consensus decision;
   
6. 
   individual socio-economic characteristics: age; gender; education; occupation; household wealth; household size.
   

Tenants’ reported satisfaction with HOA services are considered as output measures. Such measures could be biased reflecting respondents’ background, experience, expectations etc. However in our case random sampling of respondents within an apartment building alleviates this problem due to limited endogenous sorting in Russian apartment buildings where a large share of apartments have been allocated in the Soviet period on a non-market basis by local governments and employers through a “quasi-natural experiment”. In what follows we operate, whenever appropriate and unless explicitly stated otherwise, with HOA averages of the above characteristics.

Analysis of variance confirms that the overall satisfaction with HOA work exhibits substantial differences from one apartment building to another — the F-test is significant at 0.1%. We illustrate these variations by the cumulative distribution curves of tenants’ satisfaction with HOA performance (building averages) calculated separately for Moscow and Perm (Figure 2a). It is noteworthy that the distribution curve for Moscow is entirely to the right of the one for Perm, which indicates that tenants’ satisfaction with the performance of their HOA is by and large higher in Moscow than in Perm (more precisely, the first distribution stochastically dominates the second).

Assessments of individual services provided by HOAs to tenants could differ from the overall assessment, but still are strongly correlated with each other (pair-wise correlations of building averages of partial performance measures are in the 0.6–0.8 range) indicating that better-performing HOAs are usually successful across the board, and those performing poorly fail on every count.

Costs of operating an HOA are measured by maintenance fees per tenant; cumulative distributions of these costs for the two cities covered by our sample are shown in Figure 2b. According to Figures 2a and 2b, Moscow-based HOAs stochastically dominate those in Perm in terms of both inputs and outputs. Therefore comparison of Moscow and Perm HOAs in terms of their efficiency (productivity) that relates outputs to inputs requires further analysis.


(a) Distribution of overall satisfaction with HOA performance



(b) Distribution of maintenance fees

Figure 2. Outputs (a) and inputs (b) of HOAs

In doing so we distinguish between two broad categories of social capital — generic and specific. Generic social capital comprises traditional ingredients such as trust, social inclusion and communication, mutual assistance etc., whereas specific social capital enables tenants to make proper use of the institution of HOA, and in particular of the decision-making procedures that such institution involves. As it was argued in Section 2, operating an HOA is a collective action and as such requires a particular type of social capital which could not necessarily be reduced to the conventional social capital components.

Our measurement of generic social capital is based on respondents’ answers about whether they can count on neighbors’ support; how often a respondent assisted his/her neighbors; how often he/she actually received neighbors’ support; how many neighbors and how well a respondent knows; and on respondents’ volunteer work to care for common property. Specific social capital is reflected in answers to the question on how active respondents are in HOA decision-making; in the reported ability to have one’s voice heard in the process; and in the ease of reconciling different views and reaching an agreement over HOA affairs.

Correlations between measures of generic and specific social capital are shown in Table 1. All correlations between the measures of generic social capital are positive and most of them statistically significant; the same is true about specific social capital measures. This is an indication that various indicators of resp. generic and specific social capital are different facets of two underlying ”social commodities”. Such commodities however are disparate characteristics of tenant communities — cross-correlations of their components are mostly insignificant.

*, **, and *** — correlations are significant at resp. 10%, 5%, and 1% levels.

Comparison of the distributions of social capital indexes across the sample shows that generic social capital stocks in Perm are somewhat higher than in Moscow (although distribution curves intersect each other so there is no stochastic dominance in this instance) — social ties are expected to be stronger is a smaller and more traditional city (Fig. 3a). However the collective ability to operate an HOA is significantly higher in Moscow than in Perm: the corresponding distributions stochastically dominate each other (Fig. 3b).

Figure 3. Generic (a) and specific (b) social capital

Figure 4. HOA quality of governance

Definitions of the main variables are listed in Appendix A, and their summary statistics presented in Appendix B.

To measure efficiency (productivity) of sampled HOAs we employ the stochastic frontier technique which is commonly used in productivity studies of various public and private sector organizations (hospitals, universities, government agencies, utilities, farms, banks etc.), but to the best of our knowledge they has not yet been applied to community management of common-pool resources. The essence of this technique is in econometric estimation of the ”production frontier” in the input-output space of production units included in the sample. It is assumed that fully efficient best-practice units make full use of the existing production opportunities and hence belong to the frontier; therefore inward deviation from the frontier is an (inverse) efficiency measure.

An important advantage of this approach is in its applicability to measuring efficiency of multi-output production units such as HOAs in the absence of market prices for individual outputs. Suppose that the output vector comprising HOA’s services to tenants belongs to a production possibility set , where the single input is the maintenance fee charged by the HOA. Efficiency (productivity) is measured by the distance function ; this function attains its maximal value of unity in the case of full efficiency, when belongs to the boundary of and decreases with moving away from the boundary inside the production possibility set.

To estimate the distance function by using the stochastic frontier technique¹⁶, the following translog specification (Lovell, 1994) was assumed:

,

After standard transformations making use of first-degree homogeneity of  in , and by adding a random error term, the output distance function can be estimated from the following equation (Coelli, Perelman, 1996):

In our dataset smallest observation units are not HOAs but tenants; this presents an aggregation problem. In the baseline specification of the stochastic frontier model the full sample of respondents was used in estimation, and “individual” efficiency measures for a given respondent were calculated and subsequently averaged across HOAs. Similar estimation was carried out while treating the data as a panel with HOAs and their members as resp. “spatial” and “time” variables. Finally, a yet another model was estimated with prior averaging across HOAs and treating the latter as non-divisible units of the sample. The first and second models were estimated for five HOA services (common facilities maintenance; plumbing; electrical work; upkeep of the backyard; waste removal); in the third one the number of services was reduced to two due to sample limitations. We used the maximum likelihood method with individual characteristics of respondents and city dummies as control variables.

Once HOA efficiency measures are derived, they can be regressed on exogenous factors, including tangible and intangible assets of HOAs and institutional parameters that are likely to affect performance. Alternately a heteroscedastic model that directly incorporates exogenous parameters can be estimated; in what follows we used both options with similar results.

5. 
   MEASURING AND EXPLAINING HOA PERFORMANCE
   

Distribution of efficiency measures of HOAs in our sample for the baseline specification of the stochastic frontier model is shown in Fig. 5a. Cumulative distribution curves for Moscow and Perm are presented in Fig. 5b. Obtained efficiency measures exhibit significant variations across the sample reflecting uneven performance of Russian HOAs observed in practice. It is noteworthy that better-performing HOAs are more common in Moscow than in Perm; reasons for such discrepancy will become clear later in this section.

Figure 5. Distributions of HOA efficiency measures

To explain HOA performance, we estimate a linear regression model with efficiency measure as the dependent variable and a set of exogenous factors affecting performance — as independent ones. Initially such model is estimated for a core set of exogenous factors, reflecting the hypotheses set forth in Section 3 and yet compact enough to ensure statistical significance for the given size of HOAs sample. These factors include two characteristics of tangible assets — HOA size measured by the number of tenants, and the age of the apartment building; two intangible assets — generic social capital measured by the perceived availability of neighbors’ support, and specific social capital measured by the technical civic competence index; and one institutional parameter — engagement of a management company.

Once the core model is estimated, we add one by one secondary factors to check their auxiliary relevance in addition to those in the core, and to ensure robustness of the core estimation. The additional factors are as follows: city dummy; share of privately owned units in an apartment building; social and economic heterogeneity among tenants; frequency of HOA general meetings and their attendance; mutual assistance among tenants; and communication among tenants. Estimation results of the core model and its extension are reported in Table 3.¹⁸

In the core specification all factors are significant at the 1% level, with the exception of generic social capital which is significant at the 5% level. Tangible assets are essential for HOA efficiency: if logarithm of building age decreases by one standard deviation, HOA efficiency rises by 0.4 standard deviations. This is an expected conclusion — newer buildings are easier to maintain. Less predictably, the regression shows that HOA size also makes considerable positive contribution to efficiency: an increase in logarithm of the number of tenants by one standard deviation improves efficiency by 0.29 standard deviations. Therefore in our sample the positive economy of scale effect is considerably stronger than the complications of resolving a collective action problem in larger communities.

In accordance with our hypotheses, technical civic competence is essential for successful operation of HOA: an increase of this intangible asset by one standard deviation improves efficiency by 0.28 standard deviations. The impact of generic social capital is of comparable magnitude (an increase of this factor by one standard deviation improves efficiency by 0.25 standard deviations) and slightly weaker significance. This quantitative similarity does not distinguish between qualitatively different transmissions mechanisms linking specific and generic social capital with HOA performance, and conditions under which these two factors are relevant; those are described in the next section.

Finally, the involvement of a management company is a strong drag on HOA performance — on the average it reduces efficiency by 0.71 standard deviations. This is consistent with massive anecdotal evidence of Russian HOAs’ capture by management companies; further discussion of this matter is also deferred to the next section.

Table 3. Factors of HOA efficiency

Standard errors are in parentheses. *, **, and *** indicate significance at resp. 10%, 5%, and 1% level.

All but one secondary factors listed above are insignificant (p-value is less than 10%) when added to the core regression; furthermore their inclusion only slightly changes the coefficients of the core set, which confirms robustness of the core estimation. The only significant secondary factor — the share of privately owned units — has the expected sign: private owners have stronger motivation to properly maintain an apartment building than municipal governments.

Insignificance of the frequency of general HOA meetings and attendance thereof indicates that what matters for outcomes is not quantity but quality (reflected by technical civic competence) of tenants’ participation in decision-making process. Significance of the share of privately owned units (the rest is municipally owned) is incentive-driven: tenants have stronger interest in good upkeep of their collective property than local governments.

The impact of inequality among tenants is negative and “almost” (at 10%) significant. This estimation is probably downward biased: direct correlation of the inequality index with HOA efficiency is 0.39, but a portion of this correlation is captured by two core factors — building age (correlation with inequality 0.29) and generic social capital (correlation –0.30). According to the arguments summarized in Section 2 this means that difficulties of collective action in heterogeneous communities overweigh the positive effect of local public goods provision by a small group of wealthiest individuals. Our data however show traces of this latter effect as well: in a sub-sample of HOAs with more profound (above the median) inequality, building size which was highly significant for the sample at large completely loses its significance — the economy of scale effect disappears because poorer tenants, even if numerous, do not make substantial contributions towards common property management.

As another robustness check, we implemented two alternative approaches to measuring the impact of the core factors for HOA efficiency. First, instead of linear regression we implemented at the second stage another stochastic frontier model;¹⁹ and second, the impact of core factors was assessed in a one-stage procedure based on a heteroscedastic stochastic frontier model which includes exogenous factors alongside inputs, outputs, and control variables (Kumbhakar, Lovell, 2000) In all three specifications the core factors remain highly significant and have coefficients of similar magnitude (Table 4, columns 1–3). Therefore our findings are robust both to changes in explanatory variables and estimation methods.

6. 
   GOVERNANCE, OUTSOURCING, AND SOCIAL CAPITAL
   

Two different types of social capital — generic and specific, whose relevance for HOA performance was established in the preceding section, work through different transmission channels and their impact is not uniform across the sample. The main purpose of specific social capital, as it follows from its definition and derivation, is to ensure efficient participation and representation of tenants in HOA decision-making process. This kind of social capital operates through the vertical transmission channel involving governing bodies of HOAs.

To find empirical evidence of such transmission we use tenants’ reported satisfaction with their HOAs boards as a measure of the quality of HOA governance and estimate a regression of this measure on the core set of explanatory variables described in the preceding section; this estimation is reported in Table 6 (column 1). All but one core factors are insignificant in such regression; the only exception is specific social capital which is significant at the 1% level and highly relevant for HOA governance: a one standard deviation increase of specific social capital improves board performance by 0.63 of its standard deviation.

Putnam (1993) argues that successful community participation in local governance builds up social capital which in its turn contributes to further success of self-government regimes. If the same is true for residents of apartment buildings, the link between technical civic competence and HOA governance could be circular, causing an endogeneity bias. In the present case however reversed causality is unlikely, since social capital is a “slow-moving institution” (Roland, 2004) and Russian HOAs have been in existence for merely a few years. Still, to be certain, we use the same instrumental variables for specific social capital as in the previous section, and present IV estimations in columns 2–4 of Table 6. F-statistics of the weak instruments test for wealth and occupation are resp. 13.90 and 9.54. When these instruments are used in combination, they are exogenous (Sargan test p-value equals 0.36) and F-statistics is 8.80. In all of the IV regressions technical civic competence remains the only significant factor and its impact increases in comparison to the OLS estimation. We can therefore consider strong association between technical civic competence and HOA governance as firmly established; in contrast, no other factors included in our analysis appear to be associated with board performance.

Table 6. Board performance factors

Table 7. Impact of social capital and governance on HOA efficiency

Table 8. Separating the impact of generic and specific social capital

Figure 6. HOA creation as a signal

Direct evidence of such capture can be found in the fact that more often than not the involvement of a management company is not a free choice of tenants: out of 53 HOAs in the sample that were established by tenants themselves, only 6, or 12%, work with management companies, whereas among 29 HOAs that were created by third parties — local authorities and developers (who often set up crony management companies), 11, or 38%, engage services of management companies. Social capital which abounds in HOAs established by tenants enables a collective defence from capture by management companies; without such defence HOAs are vulnerable to capture and cannot free themselves from unwanted services.

7. 
   CONCLUDING COMMENTS
   

The vast common-pool resource literature shows that collective ownership and management regimes deliver highly uneven outcomes which depend on the ability of user communities to resolve a collective action problem inherent to such regimes. Our analysis reveals the same pattern across Russian homeowners associations and sheds new light on the causes of successes and failures of community management of common-pool resources and local public goods. Government support, enabling legislation, saliency of residential housing and fulfillment of other successful design principles make HOA outcomes contingent on the capacity of tenants for self-organization. Multiplicity of HOAs operating under comparable conditions presents a valuable opportunity to identify factors reflecting and identifying such capacity, and measure their impact and relevance.

A stochastic frontier estimation of HOA efficiency reproduces significant performance variations which are related to HOAs’ tangible and intangible assets and institutional regimes. Our identification of exogenous factors affecting HOA performance is robust and carries strong explanatory power. Consistent with the literature, social capital is shown to be highly relevant for collective management of residential housing infrastructure. We refine this general dictum by identifying generic and specific stripes of social capital — the latter enables the tenants to make proper use of the official mechanisms and procedures of HOAs and ensure accountable HOA governance, whereas the former is mobilized when HOA governance breaks down and informal grassroots alternatives rise in their significance.

In the absence of social capital and especially its specific variety the institution of HOA could become a dysfunctional ‘empty shell’ prone to misuse and capture. Management companies which are expected to provide professional services to HOAs are often turned into capture tools, especially when they possess a near-monopoly market power and tenants cannot properly monitor the performance of such companies and protect their collective interests. Russian HOAs are thus hampered by a “twin deficit” of social capital and competition among management companies, which highlights the importance of complementary input markets for the success of (collective) property rights reform.

Tenant communities in Russia differ in their social capital stocks and hence the ability to successfully operate an HOA. Voluntary creation of HOAs is a credible collective signal of the availability of sufficient social capital among the tenants. When such signal is absent and an HOA is imposed upon the tenants, its performance is more often than not unsatisfactory. In such cases, given social capital’s limitations, other management and ownership regimes could be second-best options. Despite of the poor track records of local governments’ management of residential housing, many in Russia view it as a preferred option to self-management (Borisova, 2010). Our study therefore agrees with the literature that emphasizes flexibility and freedom of choice of institutional regimes as essential requirements for successful governance of the commons (Ostrom, 2005) — attempts to massively and prematurely introduce “best-practice solutions” without matching social and institutional prerequisites could lead to frustrating outcomes, and offering financial rewards for accepting an otherwise unwanted and unappreciated regime could make matters even worse.

Limitations and uneven allocation of civic competence in today’s Russia revealed by our analysis is consistent with the malfunctioning of the country’s democratic institutions at the national, regional, and local levels over the first two decades of the Russian post-communist history. The ability of operate HOAs could be considered as a litmus test of a society’s democratic maturity. However civic culture is not just a constraint on community management of residential housing, but also a likely output of HOAs,²³ since people gain democratic experience by way of participating in public decision-making. The history of Russian HOAs is probably too short to produce clear evidence of such contribution of HOAs to social capital accumulation, and analysis of this process should be deferred to future research.

Agrawal, A. (2002). Common resources and institutional sustainability. In: E. Ostrom, et al. (Eds.) The Drama of the Commons. Washington, DC: National Academy Press, 41–85.

Agrawal, A., & Yadama, G. (1997). How do local institutions mediate market and population pressures on resources? Forest panchayats in Kumaon, India. Development and Change, 28 (3), 435–465.

Alesina, A., & La Ferrara, E. (2000). Participation in heterogeneous communities. Quarterly Journal of Economics, 115 (3), 847–904.

Algan, Y., Hémet, C., & Latin, D. (2011). Diversity and public goods: A natural experiment with exogenous residential allocation. IZA DP Working Paper No. 6053.

Almond, G., & Verba, S. (1963). The civic culture. Political attitudes and democracy in five nations. Princeton: Princeton University Press.

Araral, E. (2009). What explains collective action in the commons? Theory and evidence from the Philippines. World Development, 37 (3), 687–697.

Baland, J.-M. & Platteau, J.-P. (2000). Halting degradation of natural resources: Is there a role for rural communities? New York: Oxford University Press.

Bardhan, P., & Dayton-Johnson, J. (2002). Unequal irrigators: Heterogeneity and commons management in large-scale multivariate research. In E. Ostrom, et al. (Eds.), The drama of the commons. Washington, DC: National Academy Press, 87–112.

Benabou, R., & Tirole, J. (2006). Incentives and prosocial behavior. American Economic Review, 96 (5), 1652–1678.

Bengtsson, B. (1998). Tenants’ dilemma: On collective action in housing. Housing Studies, 13 (1), 99–120.

Besley, T., Burchardi, K., & Ghatak, M. (2012). Incentives and the de Soto effect. Forthcoming in Quarterly Journal of Economics.

Bjornskov, Ch. (2006). The multiple facets of social capital. European Journal of Political Economy, 22 (1), 22–40.

Borisova, E. (2010). Russian homeowners associations: An economic analysis. Mimeo. (in Russian).

Borisova, E. (2011). Decision to set up an HOA: Empirical evidence. Applied Econometrics, 24 (4), 48–57. (in Russian).

Borisova, E., Peresetsky, A., & Polishchuk, L. (2010). Efficiency analysis of non-profit associations by stochastic frontier methods: the case of homeowners associations. Applied Econometrics, 20 (4), 75–101. (in Russian).

Boycko, M., Shleifer, A. & Vishny, R. (1995). Privatizing Russia, Cambridge: MIT Press.

Chen, S.,& Webster, Ch. (2005). Homeowners associations, collective action and the costs of private governance. Housing Studies, 20 (2), 205–220.

Coelli, T., & Perelman, S. (1996). Efficiency measurement, multiple-output technologies and distance functions: With applications to European railways. CREPP, Université de Liège.

Dietz, T., Ostrom, E.,.& Stern, P. (2003). The struggle to govern the commons. Science, 302 (12), 1907–1912.

Di Pasquale, D., & Glaeser, E. (1999). Incentives and social capital: Are homeowners better citizens? Journal of Urban Economics, 45 (2), 354–384.

Glaeser, E., & Sacerdote, B. (2000). The social consequences of housing, Journal of Housing Economics, 9 (1–2), 1–23.

Glaeser, E., Ponzetto, G., & Shleifer, A. (2007). Why does democracy need education? Journal of Economic Growth, 12 (2), 77–99.

Glazunov, S. (2008). Housing issues in Russia: Problems and prospects. Moscow: Omega-L. (in Russian).

Grafton, Q. (2000). Governance of the commons: A role for the state? Land Economics, 76 (4), 504–517.