Individualized metering and the influence of other endogenous and exogenous factors on water consumption in social housing

Water conservation has been a matter of concern in recent decades and, thus, a holistic approach that includes policies, strategies, and activities is essential (Weerasooriya et al. 2021). In the social housing context, social innovations to promote water conservation can potentially lead to financial savings for one of the most vulnerable segments of the population (Marchesi & Tweed 2021). Over the last few decades, research has sought to understand the effect of measurement systems on consumption.

Malan & Crabtree (1987) analyzed the effects of individual meters in two cities in South Africa with different approaches. In Pretoria, the individualization of metering resulted in a 27% reduction in water consumption. In East London, they found that apartments with individual metering consume 20%–25% less water than apartments with collective metering.

Rajala & Katko (2004) verified that the median per capita water consumption in Finland is lower in buildings with individual metering (120 L/capita/day) than in buildings with shared metering (140–150 L/capita/day). Likewise, in Seville, Spain, Castillo-Manzano et al. (2013) observed that a 41.71% increase in the ratio between water meters and dwellings (caused by individualization) was responsible for a 37.52% decrease in water consumption. Recently, in a longitudinal study on domestic hot water consumption in Lublin Voivodeship (Poland), Canale et al. (2023) found that, after the individual metering system installation, the average daily demand for water heating decreased by 14%, and the volume withdrawn by the buildings dropped by 32%. Although domestic hot water also involves energy prices for heating, individual metering was proven to reduce consumption from both the energy and the water perspective.

From a system perspective, individual metering can help identify leakages and reduce water waste (de Souza Guedes & Athayde Júnior 2021). However, the choice may not be obvious from engineers' and builders' perspectives, since individual metering costs 18% more than regular collective metering (de Souza Guedes & Athayde Júnior 2021). According to Lima et al. (2024), in Brazil, water consumption measurement in multifamily buildings is commonly based on the installation of one water meter for several housing units. As a result, the water bill for the entire building is divided among residents (Lima et al. 2024). Ilha & Monteiro (2015) state that the implementation of individual systems is also an opportunity to reduce waste, since residents will pay for the volume of water they actually consume. In fact, Brazilian Federal Law 13.312 (Brazil 2016) determines that new buildings must follow some sustainability criteria, adding the requirement for individualized metering for new multifamily buildings after 2021. Pre-existent buildings are not required to change their water metering to individual.

The installation of water meters can make saving money a motivation for using water efficiently (Shan et al. 2015). Abu-Bakar et al. (2021) state that metering is one of the most effective demand-side management tools to encourage water conservation. Therefore, measures of water conservation that have a positive effect on social housing and can reduce household's monthly expenses should be investigated.

In terms of socioeconomic aspects, income is a frequently used determinant with an elevated impact on household water consumption (Cominola et al. 2023). A previous study on water consumption in a social housing complex in Florianópolis, southern Brazil, found no correlation between income and water consumption (Marinoski et al. 2014). According to the case study by Marinoski et al. (2014), water consumption was more related to the household's lifestyle than to income itself. Garcia (2011) also observed no correlation between income and water consumption when analyzing low-income households, which was explained by the fact that the income did not vary as much within the case study. In this study, we observed how individual metering affected families with different income levels.

Besides endogenous variables, water consumption may also be related to environmental factors (Abu-Bakar et al. 2021). Therefore, the effects of exogenous variables, such as outdoor temperature, must be analyzed. As highlighted by Reis et al. (2023), environmental and climatic variables are often overlooked as potential determinants of water consumption, despite their significant impact on human habits, such as bathing and laundry frequency. The review study by Cominola et al. (2023) classified temperature as a less frequently used variable in studies related to household water consumption. Temperature, like other external variables, is often used in urban water consumption studies and just sometimes used in household water consumption studies, so further research is needed to check the latter (Cominola et al. 2023).

With climate change, tropical countries, like Brazil, can face warmer weather conditions than what is usually experienced (Sanzana et al. 2023), which may have an impact on water consumption. According to Niazmardi et al. (2023), trend and seasonality patterns of water consumption in different regions can give more information about this phenomenon. For this reason, climate variables should be considered in the modeling process.

This case study aims to evaluate the impact of consumption billing based on individual water meters in a social housing complex in the city of Joinville, southern Brazil, by controlling the effects of the temperature and the COVID-19 pandemic on water consumption. This study also aims to understand if households with different income ranges respond differently to the individualization of water metering and billing. To the best of our knowledge, this article is the first (i) to explore how income relates to individual metering among low-income households and (ii) to monitor a change from collective to individual billing in social, multifamily buildings at the household level.

In the first stage of the research, residents of the 158 households were interviewed. The interviews were conducted weekly in July and August 2019, between 8 a.m. and 6 p.m., and took approximately 4 weeks. The number of residents and income were obtained during the interviews, and possible changes over time were disregarded. A family must earn no more than two minimum wages to be qualified for a residential unit in this social housing program. Nonetheless, there is no guarantee that a household's income will remain constant after the benefit is granted. Although there are quite strict rules for income and transfer of ownership of housing units in this type of housing project according to the Brazilian Federal Law 11.977 (Brazil 2009), this is a limitation of this study. Water consumption data were collected over a 1,429-day period, from 1 January 2018 to 30 November 2021. Until 21 August 2020, the water billing system was based on a collective water consumption meter, with subsequent apportionment of the consumption charges between the households. An individual measurement system was then implemented, using one water consumption meter per household. Thus, water consumption measurements were carried out in two periods: (i) with apportionment billing from 1 January 2018 to 21 August 2020, and (ii) with individual measurements and billing from 22 August 2020 to 30 November 2021. Water consumption data were obtained through a telemetry system from the Water Utility, Companhia Águas de Joinville, with the interviewed residents' consent. The research project was approved by the Santa Catarina State University's Ethics Committee (CAAE 14122819.4.0000.0118).

Data exploratory analysis, including descriptive statistics and graphics, was applied to assess the impact of individual water metering and billing in water consumption. The water bill individualization impact was also analyzed for families with different per capita incomes. The Wilcoxon–Mann–Whitney test was used to verify whether the water bill individualization resulted in a significant reduction in water consumption for families with incomes up to 1.5 and above 1.5 minimum wages per capita.

The model's dependent variable is water consumption per capita (L/person/day) in the tth month. is the intercept. The regressor that corresponds to the intervention (in the billing system) is (a dummy variable, with billing system based on collective water metering = 0; and on individual metering = 1). corresponds to the estimate regarding the change in the billing system. The parameter ⁠, when positive and significant, indicates an increase in the post-intervention response variable; when negative ⁠, it suggests a reduction; and when not significant, the event did not affect the outcome and the time series has a stationary trend (Antunes & Cardoso 2015; Alves-Costa et al. 2023). The other regressor variables included in the model are ⁠, corresponding to the outdoor temperature (in °C) and ⁠, a categorical variable denoting the COVID-19 waves. Their respective parameters and are related to these additional regressor variables, the outdoor temperature, and different waves of the COVID-19 pandemic. The error term is ⁠. The Prais–Winsten method assumes that the residuals (errors) follow a first-order autoregressive process, and a linear transformation is applied to the response and explanatory variables (⁠ and₎ to remove the error term correlation (Bottomley et al. 2023).

The inclusion of the outdoor temperature as a control variable (Bernerth & Aguinis 2016) enabled us to isolate the impact of the metering system type on water consumption while mitigating potential influences from temperature variations. As the measurement system change occurred in August 2020, during the COVID-19 pandemic, we included the pandemic waves in the model to isolate the effects of the measurement system alteration from a possible effect of the pandemic. Data from January 2018 to March 2020 were considered as before the COVID-19 pandemic. The pandemic waves were based on the studies by Bastos et al. (2021), Zeiser et al. (2022), and Castro & Valverde (2022), as follows: the first COVID-19 wave encompassed data from April 2020 to October 2020, the second from November 2020 to April 2021, and the third from May 2021 to November 2021. It is important to consider the control variables before the data collection in the experiment design, or through statistical analysis in retrospective studies (Nielsen & Raswant 2018), as in this research. According to Nielsen & Raswant (2018), this enables controlling the effects of these (control) variables, avoiding the occurrence of type II errors, which corresponds to falsely concluding that the variable of interest (in this case, the water metering system) has a causal relationship with the dependent variable (water consumption). The statistical analysis was performed using R software (R Core Team 2023), with the aid of the Prais package (Mohr 2021). The significance level adopted was 5%.

The average water consumption per capita, for both periods, is higher for families with a per capita income greater than 1.5 minimum wages. Table 3 shows that families with a per capita income of up to 1.5 minimum wages had a 20.92% average reduction in their water consumption, while those with more than 1.5 minimum wages per capita saw a 16.47% reduction. As Figure 3 shows, in the case of families with a higher monthly income, there was a reduction in maximum consumption after adopting the billing system with individual consumption measurements. The results of the Wilcoxon–Mann–Whitney test showed that the differences in water consumption in the periods before and after the individualization of water bills for both income groups were significant.

The income per capita is inversely proportional to the number of residents per household, known to impact water consumption (Abu-Bakar et al. 2021; Cominola et al. 2023). All households with more than 1.5 minimum wages per capita are either single or dual households (60 and 40%, respectively). Households with less than 1.5 minimum wage per capita are diverse: 8% have one person, 22% have two, 36% (the largest group) have three, 22% have four, 10% have five, and only 2% have six or more occupants.

From January 2019, an increase in per capita water consumption is observed. This increase was reflected in the water bill, which led the condominium administration to request the billing system to be based on water consumption measurements for each housing unit. The reasons for such an increase are beyond the scope of our work. It is known that during the interviews (which happened in July and August 2019), some dwellers speculated that some households were benefitting from the collective billing system to consume more water without a directly proportional cost burden. Still, the reasons for this increase in consumption before the individual billing system are a limitation of our study. Regardless of that, after the individualization of the water bills and the first wave of the pandemic, water consumption was reduced to a level lower than that of 2018.

During the first wave of COVID-19, median per capita water consumption was higher than that observed in the pre-pandemic period. However, the graph shows a reduction in water consumption after the individualization of the billing system, especially during the last two waves of the pandemic. Other studies have also reported an increase in water consumption in the initial months of the COVID-19 pandemic, with a subsequent reduction. Ribas et al. (2024) evaluated domestic water consumption in the city of Girona, Spain. The authors mention the reduction in water consumption in the period from March to June 2021 compared to the same months in 2020. In São Leopoldo, Brazil, Tavares et al. (2023) reported an increase in residential water consumption in the first months of the pandemic, with a gradual return to pre-pandemic levels in the second half of 2020. Lima et al. (2024) evaluated the installation of an individualized water consumption system in a building in Brazil. The authors reported a progressive reduction in water consumption after the installation of the individual metering system (14% in 2019, 14% in 2020, and 25% in 2021 during the COVID-19 pandemic).

Regarding the pandemic, median water consumption increased during the first COVID-19 wave, although the average water consumption used in the Prais–Winsten model did not show any significant increase during this period. The change to an individualized billing system occurred during the first wave, and the reduction caused by its adoption was significant in the model, which did not occur with the COVID-19 waves themselves.

The negative sign of the parameter representing the metering system shows that the model captured the reduction in consumption after adopting the individual metering and billing systems. This result was significant (p-value < 0.001) and indicates that there is a decrease in water consumption⁠. Both the temperature and the individual metering system were expected to be related to the water consumption behavior. Indeed, the model shows that individual metering played a significant role in reducing water consumption.

Temperature was related to water consumption but not in a significant manner. The positive sign of the temperature coefficient in the model shows that the increase in temperature is associated with the increase in water consumption. However, this result was not significant at the 5% level. In the literature, results regarding the influence of temperature on household water consumption vary, while a pattern is observed for individual metering. Studies in Europe show no significant effects of average temperature on household water consumption (Slaviková et al. 2013; Bergel & Mlyńska 2021). In the state of Goiás, Brazil, a day's highest temperature was found to be significantly correlated with household water consumption, but the average temperature was not (Reis et al. 2023). Since the temperature can be relevant as a driver of water consumption, although further studies are needed (Cominola et al. 2023), the use of the temperature as a control variable was a reasonable choice.

Changing the water metering system used for billing purposes provided consumers with more information about water consumption. Brent & Ward (2019) reported that the mode of billing impacts water consumption. It is important, therefore, to improve the perception of water use and pricing. Ensuring the supply of drinking water in quantity and quality, with equity for all, is a challenge that all countries, whether developed or developing, face, or will face.

Furthermore, analyzing the income effects on water consumption is important as, according to Medwid & Mack (2021), household income was the most important variable to explain the change in consumption behavior when water bill costs increased. Changing the billing system based on collective meters to individual ones allows users to obtain real information on their water consumption and, thus, consumers can become more aware and avoid wasting water. Individual metering may represent a decrease in the water costs for residents with lower water consumption. For Medwid & Mack (2021), low-income families are particularly sensitive to possible increases in water bills, as they have limited capacity to absorb extra costs or difficulty reducing water consumption to compensate for increased costs. The case study evidenced that the water billing system influences the per capita water consumption in the social housing complex under analysis. Thus, implementing individual metering systems can lead to water conservation, in addition to the financial savings that a reduction in water consumption can provide to the residents of social housing complexes.

This case study investigated the difference in water consumption with collective and individual metering systems in 158 households in Joinville, southern Brazil, which are part of a multifamily social housing complex. Water consumption data were analyzed using statistical analysis, the Wilcoxon–Mann–Whitney test, and a Prais–Winsten model. There was a significant decrease in per capita water consumption when the billing system changed from apportionment payment to individual billing, which was verified through individual consumption metering before and after the transition.

To the best of our knowledge, the literature also consistently shows that individual metering and billing are associated with a reduction in water consumption. Furthermore, we used outdoor temperature and the COVID-19 pandemic waves as control variables. Even with the income range restriction, due to the low-income program regulation, differences were found among the two groups of income. The group with the lower income showed a greater reduction in per capita water consumption with the change to individual metering and billing. This result can also be related to occupancy, since households with one or two members have limited options to reduce per capita water consumption by, for instance, shared use of water for doing the laundry, food preparation, and cleaning. Such limitation is an opportunity for future studies with larger samples in which it is also possible to evaluate the income effects while the number of occupants remains constant.

Finally, our results showed a reduction in water consumption when the payment for the water service is associated with actual consumption, which could be tested and extended to other locations. Our results support billing based on individual metering for water conservation, but further studies are necessary to understand its socioeconomic consequences. For instance, individual rather than collective water metering and billing may have a social impact. Larger households, which in this case study have lower income per capita, may have an increase in their expenditure on water, while households with one or two members may likely reduce such basic service expenditure. We recommend extending this research to encompass more socioeconomic factors and consumption habits. Finally, it is important to highlight that water consumption data were collected from January 2018 to November 2021 and that the interview stage occurred during 2019. Therefore, any changes in occupancy during the analysis period were disregarded. Although the number of residents per housing unit is consistent with the data from the latest censuses, this is a limitation of this study.

The authors acknowledge Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, grant number 423090/2021-6) and Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC, grant number 2023TR000334) for the financial support. This study was also funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES – Finance Code 001. The authors also acknowledge Companhia Águas de Joinville for making this research possible by sharing the water consumption data. The authors also thank all the dwellers from the studied buildings who accepted sharing their water consumption data and participated in the survey.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.