metadata: authors: 0: author: Batana,Yele Maweki
1: author: Jarotschkin,Alexandra
2: author: Konou,Akakpo Domefa
3: author: Masaki,Takaaki
4: author: Nakamura,Shohei
5: author: Viboudoulou Vilpoux,Mervy Ever
count: Congo, Democratic Republic of
docna: 0: docna: Profiling Living Conditions of the DRC Urban
            Population : Access to Housing and Services in Kinshasa Province
docty: Policy Research Working Paper
subtopic: Hydrology,Urban Housing and Land Settlements,Urban Governance and Management,Municipal Management and Reform,Urban Housing,Health and Sanitation,Small Private Water Supply Providers,Sanitary Environmental Engineering,Environmental Engineering,Sanitation and Sewerage,Engineering,Water Supply and Sanitation Economics,Water and Human Health,Town Water Supply and Sanitation,Educational Sciences,Natural Disasters
teratopic: Communities and Human Settlements,Public Sector Development,Urban Development,Health, Nutrition and Population,Education,Environment,Science and Technology Development,Water Resources,Water Supply and Sanitation
abstracts: cdata!: This paper examines living
            conditions&#x2014;mainly access to infrastructure and
            basic services&#x2014;in Kinshasa, by focusing on how
            they vary within the city and how they are related to
            household characteristics. First, drawing on a household
            survey conducted in the capital province in 2018, the paper
            shows that many Kinshasa residents live with substandard
            housing and inadequate levels of access to infrastructure
            and basic services. Second, the level and quality of access
            to basic services are highly correlated with
            residents&#x2019; consumption and education levels, as
            well as their neighborhood characteristics. Third, despite
            the presence of negative externalities from the high
            population density, poor households benefit from living in
            dense neighborhoods by gaining a minimum level of access.
            The paper argues that it is imperative to increase the
            supply of affordable housing to lessen the inequality of
            access to services in Kinshasa.
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repnme: repnme: Profiling Living Conditions of the DRC
            Urban Population : Access to Housing and Services in
            Kinshasa Province
display_title: Profiling Living Conditions of the
            DRC Urban Population : Access to Housing and Services in
            Kinshasa Province
listing_relative_url: /research/2021/11/33631243/profiling-living-conditions-drc-urban-population-access-housing-services-kinshasa-province
topicv3: Sanitation m1326453 2000,Water m1326522 137,Education m1327048 470,Land and Housing m1327648 817,Flood m1503315 3216
publishtoextweb_dt: 2021-11-19T00:00:00Z
dois: 10.1596/1813-9450-9857
historic_topic: Communities and Human Settlements,Public Sector Development,Urban Development,Health, Nutrition and Population,Education,Environment,Science and Technology Development,Water Resources,Water Supply and Sanitation
origu: EFI-AFR1-POV-Poverty and Equity (EAEPV)
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4: author_key: 1389722
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model_extractions: Policy Research Working Paper 9857

# Profiling Living Conditions of the DRC Urban Population Access to Housing and Services in Kinshasa Province

_Yele Maweki Batana Alexandra Jarotschkin Akakpo Konou Takaaki Masaki Shohei Nakamura Mervy Ever Viboudoulou Vilpoux_ Poverty and Equity Global Practice November 2021
Policy Research Working Paper 9857

# **Abstract**

This paper examines living conditions—mainly access to infrastructure and basic services—in Kinshasa, by focusing on how they vary within the city and how they are related to household characteristics. First, drawing on a household survey conducted in the capital province in 2018, the paper shows that many Kinshasa residents live with substandard housing and inadequate levels of access to infrastructure and basic services. Second, the level and quality of access to basic services are highly correlated with residents’ consumption and education levels, as well as their neighborhood characteristics. Third, despite the presence of negative externalities from the high population density, poor households benefit from living in dense neighborhoods by gaining a minimum level of access. The paper argues that it is imperative to increase the supply of affordable housing to lessen the inequality of access to services in Kinshasa.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ybatana@worldbank.org or snakamura2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Profiling Living Conditions of the DRC Urban Population: Access to Housing and Services in Kinshasa Province**

Yele Maweki Batana, World Bank Alexandra Jarotschkin, World Bank Akakpo Konou, World Bank Takaaki Masaki, World Bank Shohei Nakamura, World Bank<sup>1</sup> Mervy Ever Viboudoulou Vilpoux, World Bank Keywords: Urban poverty; multidimensional poverty; spatial inequality, informal settlements JEL Codes: C18, D31, I14, I24, I31, I32

> 1 Corresponding Author: snakamura2@worldbank.org. We would like to thank Andrew Dabalen, Ruth Hill, Kirsten Hommann and Clarence Tsimpo for useful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent.
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percent of the entire poor population in the country.<sup>4</sup> As reported in Batana et al. (2021), the poverty incidence in Kinshasa is likely to have risen since 2012.

Kinshasa has not been able to reach its full potential due to its disconnection from the global economy and unplanned urbanization. Cities may have several advantages by allowing people to be closer to jobs and generating economic opportunities and enhancing productivity due to agglomeration of economies. Public goods such as infrastructure and basic services can be provided more efficiently and equitably when populations are large and densely packed (World Bank 2018b). However, Kinshasa remains largely a local city—as opposed to a global city that is open to regional and global trade, markets, and investments—and it produces fewer internationally tradable goods and services (60 percent) than other world cities (Lall, Henderson, and Venables 2017).

Another feature characterizing the city is a proliferation of precarious areas, abandoned parts of urban Kinshasa that suffer from rough terrain, poor services, and various environmental/disaster risks. As described in Section 2, this paper defines neighborhoods are identified as precarious if they have characteristics that are not desirable for habitation. The expansion of precarious areas in Kinshasa is a rather recent phenomenon, instigated by the unprecedented population growth that the city has experienced over the past half century (Nzuzi 2017). Precarious areas concentrate particularly in newly built residential areas in suburban/peri-urban areas that lie mainly outside the 5 km radius around the city core. These precarious areas are home to roughly 60 percent of the Kinshasa population.<sup>5</sup> This paper aims to present new data on the spatial and demographic patterns of the city and the spatial and socio-economic variation in access to services. Key questions addressed in this note include: _what is the standard of living conditions in urban Kinshasa? How do access to infrastructure and basic services vary depending on households’ socio-economic characteristics and residential locations?_ In examining these questions, this paper briefly reviews the patterns of urban growth and proliferation of precarious neighborhoods in Kinshasa (Section 2). This paper then shows various aspects of living conditions of Kinshasa residents by their socio-economic characteristics and residential locations (Section 3). The paper also examines in Section 4 the degree to which spatial or demographic characteristics drives access to services, thereby shedding light on whether area-focused interventions or individual-interventions are needed to improve service outcomes. It identifies the areas of the city where investments are particularly needed. Finally, the analysis estimates the cost to poor households of getting housing with service provision and located near the economic core of the city.

Key findings are summarized as follows. First, worse living conditions are endured by poor and/or less educated households in precarious and/or peri-urban neighborhoods. Second, inequality in access to water is driven by locational factors, while sanitation access depends on household characteristics, such as the education levels of household heads. Third, while there is a concern about negative externalities from Kinshasa’s extremely high population and built-up density, poor households benefit from density to some extent, as indicated by a higher chance of their gaining access to basic services in dense neighborhoods. Finally, high housing costs in neighborhoods

> 4 The numbers of the poor in 2017 are simulated based on the projected population from the National Institute of Statistics ( _Institut National de la Statistique_ , INS).

> 5 Based on population estimates from WorldPop (2013).

3
closer to the city core restrict access to economic opportunities and services among low-income households.

# **2. Data and background**

## **2.1 Data**

The analysis of this paper relies primarily on a recently collected household survey in Kinshasa. Conducted by the INS of the DRC under the World Bank’s statistical operation in the country, it is an integrated survey, consisting of two surveys, targeting individuals and households. The first part of the survey includes sociodemographic characteristics, education, health, living condition, employment, urban agriculture and non-agricultural household entrepreneurship. The second component is the household consumption survey.

The Kinshasa Household Survey was collected in two 18-day phases between mid-October and the end of November 2018 on a sample of 2,592 households. Given the outdated last population census—which was collected in 1984—the sampling method for the Kinshasa Household Survey was based on high-resolution satellite imagery. A two-stage stratified sampling design with equal allocation of first-degree EAs was used for the survey. Kinshasa had been divided into two clusters: urban and rural. The 23 communes/districts of the city were considered as urban and some areas of Maluku, as part of the survey, were considered rural.

Two criteria were used to stratify the city (a) the building density of EAs and (b) typology of Kinshasa areas. The stratification of EAs according to the density criterion was carried out as follows: 1) Low density; fewer than 10 buildings per hectare, 2) Medium density; from 10 to 20 buildings per hectare, and 3) High density; more than 20 buildings per hectare. Precarious neighborhoods are those not suitable for habitation. In the survey, precarious areas are defined based on their characteristics of dwellings and geographic and environmental features.

- **Precarious areas.** This is a neighborhood whose dwellings are built by the occupants on land acquired in undeveloped areas, on farmland and market gardens, or on uneven ground (exposure to erosion and flooding). Buildings are anarchic and the use of recycled materials (cardboard, plastics, and sheet metal) is common. Most often, they are located in parts of the city abandoned by the more affluent categories: on steep slopes or near industrial areas, which makes them all the more dangerous and where misery is concentrated. Collective facilities (water, electricity, sanitation, and transport) are reduced and availability is low.

- **Non-precarious areas.** These are neighborhoods where homes are self-constructed, constructed by stages, or constructed with the intervention of a contractor. They are also neighborhoods built entirely by the state for social housing in accordance with urban standards (drainage system, access to water and electricity, tracing roads, and so on) and habitat.

Each enumeration area (EA) is then classified as either precarious or non-precarious by the knowledge of local experts based on the definition above (Figure 2).

The sample was drawn from the following seven strata: (a) low-density non-precarious zone, (b) medium-density non-precarious zone, (c) high-density non-precarious zone, (d) low-density 4
precarious zone, (e) medium-density precarious zone, (f) high-density precarious zone, and (g) rural area.

While the Kinshasa Survey is prepared with an innovative approach, the use of the survey requires caution against its data quality and comparability. It is extremely challenging to design a sampling survey to collect information about households and individuals that represents Kinshasa, due to the lack of a recent population census. The sampling of the Kinshasa Survey, which is the primary data for this study, was prepared by drawing on satellite imagery. In addition, unlike the 1-2-3 household survey of 2012, the stratification of the 2018 survey explicitly captures precarious areas. Despite such an innovative sampling approach, the Kinshasa Survey has concerns about the data quality because of a problem in the data collection process (see Batana et al. 2021 for detailed descriptions). Considering all these factors, this study does not report any absolute number of population based on the survey and does not make a direct comparison between the Kinshasa Survey and the 1-2-3 household survey.

This note also draws on other existing survey data, such as the 1-2-3 household survey of 2012, Water, Sanitation, and Hygiene (WASH) survey, and Demographic and Health Survey (DHS) 2013–2014, as well as satellite imageries and other spatial datasets, to capture trends in the levels and qualities of services and also identify spatial determinants of service access.

Throughout the paper, we follow the definitions of water and sanitation services as provided by World Health Organization (WHO)/United Nations Children’s Fund (UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation, and Hygiene. To distinguish quality of water and sanitation access, they are classified into the following categories.

In the case of water access, it is classified into (a) improved water; drinking water that includes piped water and protected wells/springs, (b) piped water; water sources coming from public/private taps at home or in neighborhoods and (c) basic water; drinking water from an improved source that is available within 30 minutes for a round trip. These categories are not mutually exclusive. For example, some households have access to improved and basic water (improved water in proximity) while others have access to improved water but not basic water (improved water not in proximity).

Regarding sanitation access, the two categories are a) Limited sanitation; access to improved facilities that are shared with other households, and b) Basic sanitation; the use of improved facilities that are not shared with other households.

5
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Precarious areas in Kinshasa are patched in different parts of the city. Unlike other major African cities (for example, Kibera in Nairobi) where precarious areas (or informal settlements) are geographically concentrated, Kinshasa has multiple pockets of precarious areas spread across its city boundary. In many parts of the city, precarious areas are located right next to non-precarious areas. As discussed more in detail throughout this report, poorer households tend to reside in precarious areas that suffer from a perennial shortage of basic services, and expanding service access to these precarious areas remains a key challenge. Both the level and quality of key basic public services in Kinshasa, such as water supply and sanitation, can easily deteriorate due to the city’s inability to cope with the rapid increase of urban population adding pressure on its critical infrastructure and public service systems. Therefore, there is an immediate and increasing need for investing in the delivery of public services and core infrastructure to improve the living standards of Kinshasa residents.

# **2.3 Socio-economic characteristics by residential locations**

Urban centers and non-precarious neighborhoods are home to wealthier households. Figure 8 shows that the distribution of households across the five different quintiles of per adult-equivalent consumption expenditures varies significantly along these spatial dimensions.<sup>9</sup> More specifically, richer households live closer to the city core while poorer ones are largely excluded from the city core or its surrounding areas (Panel A). Similarly, non-precarious areas tend to host a higher proportion of richer households compared to precarious areas (Panel B). As discussed further in the following sections, these rich neighborhoods come with higher housing prices/rents and are largely inaccessible to poorer households who have no choice but to live in more affordable and yet precarious areas that are disconnected from core urban areas and suffer from poorer living conditions.

The wealthier status of those residents in the preferable neighborhoods allows them to purchase better access to key public services, which residents in the non-preferable neighborhoods may not be able to afford. While households living in the city core or non-precarious neighborhoods enjoy better access to services, their spending on these services is accordingly much larger than those households living in the peri-urban or precarious neighborhoods. For instance, the amount that residents in non-precarious neighborhoods spend on bills for electricity and stocking water (for own consumption) are 40 percent and 54 percent higher, respectively, than residents in precarious neighborhoods.

> 9 The consumption expenditures are based on the 2018 Kinshasa Survey with adjusted sampling weights. See Batana et al. (2021) for details.

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_Access to public services by socio-economic characteristics_ The level and quality of public services are closely associated with households’ socio-economic characteristics. Figure 13 describes how the level and quality of access to key services, such as water (panel A); sanitation (panel B); and solid waste management (SWM), electricity, and the Internet (panel C); vary depending on demographic and income variables. For instance, as the dependency ratio and educational levels of household heads are strongly correlated with their income levels, it is not surprising to see the stark contrast in the level and quality of access to services between the poor and non-poor households. The gap is particularly wide between those with university degrees and others (even those who completed secondary education). No significant gap is observed along the gender of household heads.

# _Access to public services by geographic characteristics_

Alongside households’ socio-economic characteristics, their residential locations are clearly associated with their levels of access to infrastructure and basic services. It is useful to distinguish the types of infrastructure and services based on the extent to which their installation is predicated on the networks of the trunk infrastructure. The access to piped water is an example of such infrastructure, as individual access to piped water relies on the networks of main water pipelines. The development of such main pipelines and the other local lines connected to them could cost less in a dense city. Infrastructure and basic services that are less network reliant include pit latrines. Septic tanks are more ideal in urban neighborhoods from a public health perspective, though fecal sludge management tends to be challenging in highly dense precarious neighborhoods. Therefore, density and precariousness may be associated with the development of infrastructure and services differently, depending on the types of infrastructure and services.

Within urban Kinshasa lie significant spatial disparities in terms of access to basic services. To illustrate this point, Figure 14, panel A shows spatial variation in access to electricity as captured by the intensity of nighttime light in 2018. It is immediately clear that much of electrification is concentrated in the city core and its immediate surroundings whereas areas farther than 15 km from the city core see very limited nighttime luminosity, which also indicates the low level of electrification. The pattern of electrification also seems to correspond with the road infrastructure network within the city. It is plausible that measures of nighttime light not only indicate access to electricity but also a higher concentration of people or economic activities along the road network.

Access to schools and hospitals are very much constrained particularly in the peri-urban neighborhoods outside the 15 km radius of the city core. Not only does the road network become sparse outside the city core and its vicinities, but public transportation is also largely unavailable for a large swath of areas beyond those core urban areas (Figure 14, panel B). This potentially limits access to essential amenities such as schools and health facilities, which are unsurprisingly also concentrated in the city core and its immediate surroundings (Panels C and D in Figure 14). Because the welfare level of households is strongly and positively correlated with proximity to the city core, it is important to expand road accessibility in the peri-urban areas to ensure that poor households can also benefit from a tighter network of public infrastructure that agglomeration economies can offer.

17
<!-- Start of picture text --> 8= vy a7 Sex st 8= as Educationa 95 3= 86Consumption1 quintile7 os a7<br>78 76 79 73 83 79 en tds Er}<br>2 re 70 ne nt<br>be 68 cr)<br>32 32 58 32 ‘ sal<br>283 28a 50 Fd28 so<br>3. 3 6 r)<br>od 2 e<br>FY o 2 a 2 FY . - 20<br>13<br>7<br>o Female Male o Preschool/Primary Secondary University o 1 2 3 4 5<br>(ER Piped water «I improved water<br>(EI Basic water = I) improved water on premises<br><!-- End of picture text --> <!-- Start of picture text --> =J Sex Education Consumption quintile<br>= i=]= = J<br>w~ w~ 5 wo~ 73<br>3232zB258<br>aa a<br>3 3 47 a7 3 3 3 3<br>3# 5# 42 3# 42<br>cry<br>a 26 25<br>20 a a e 19<br>1" "1 6<br>7<br>4<br>1<br>o Female Male o Preschool!Primary Secondary University o 1 2 3 4 5<br>(ER Limited and basic sanitation (I) Basic sanitation<br><!-- End of picture text --> <!-- Start of picture text --> 8 Sex 8 Education 8 Consumption quintile<br>wo~ wo~ s wo~ 73<br>w 61 PY w s<br>: 58 : 57 s7 2 58 s7<br>: 3 50 41 : 8 £8 © 43<br># # “ * #<br>uM<br>ES) * * 28<br>»NwyN»N<br>510<br>. 5 1 _ a * 0 4<br>o co os<br>Female Male Preschool/Primary Secondary University 1 2 3 4 5<br>GEE sw sevice EEE) clectncity<br>[EEE] The internet<br><!-- End of picture text -->
<!-- Start of picture text --> Legend :<br>~—— Road Network . a<br>5km x<br>10km 2.<br>. 45k5 me .Pa<br>Nighttime Light (2018) m /<br>Valuemen High— : 601.91 :Saraeae| 4<br>—<br>a<br>|~ Low: 1.64 ee og- \<br>4 ye ‘ia Jae m1) A -><br>g ‘ & = 4 ( 3 K\ 4<br>x ; F on 3 6 I<br>4 Con 7 ‘<br>0SSS275 55 " 16.5res22 os| Oo rn<br><!-- End of picture text -->

<!-- Start of picture text --> Legend<br>Road Network P<br>° Schools<br>5km<br>10km<br>15km<br>Dist. to Schools.<br>0-178<br>179 - 299<br>465300 -- 797464 {Ns—<br>>=798 , 4s<br>oOis WS Peaiy alt<br>-ge “an q aee<br>SL oy 6<br>? é<br>0 275 55 1 16.5 2 Om ae<br><!-- End of picture text --> <!-- Start of picture text --> a ;<br>iss .<br>Legend os<br>.<br>e Pax Van Stop SY<br>* Bus Stop t/t<br>a iy<br>—¢ MinibusRoadRoad NetworkNetwor Stop oayeaett, ee<br>5km _ ai i ~~<br>10km Soo fe<br>J \e USEPSSY AQ QS<br>\ \ fe i ef \ LAB<br>a Noe r i) 4<br>0 Con 4 .<br>0.rrr275 55 " 16.5 22 |as Om ba}<br><!-- End of picture text -->

<!-- Start of picture text --> Legend<br>Road Network<br>e Health facilities<br>|5km<br>10km<br>15km<br>Dist. to Health Facilities<br>0-225<br>226 - 409<br>410658 -- 970657 f a oN<br>>=971 ” y.<br>BtPeteMIRphd vn‘<br>4 Wee.5 cf<br>+N aan é<br>fa é<br>0 275 56 1" 16.5 2 Om tain<br><!-- End of picture text -->
<!-- Start of picture text --> ane<br>e<br>.<br>i4:<br>‘<br>a \<br>/ raa ;!\<br>ner ~oe aP Legend« ced<br>; al 410k<br>‘ 15km<br>- Basic water on pramise, Ecoli free<br>~~ 66.1 - 66.7<br>oe i1MMmm 102-19154.845.719.2-45.6 -- 54.766.0<br>Na<br>_.<br>myf epy ;ed<br>oc : of [a<br>a275 55 " ——— 7s Za Or :+ Fe<br><!-- End of picture text --> <!-- Start of picture text --> ° 98 9797<br>eS 90 90 9588 93.92 96 93 39<br>84 re<br>ofCy Lo 67 66<br>[=]<br>o 55<br>3.538 45 49 46 47<br>=6<br>lo 23 24<br>N 15 19<br>10<br>8<br>° SF> co2 e e 1 we oS!& Ry)&R<br>& e RS NaS iS ~<br>Rs se eS ES<br>HE Improved water [ME Basic. water<br>[19 Basic water on premise, Ecoli free [7] Safely managed wate<br><!-- End of picture text -->
<!-- Start of picture text --> Distance from City Core<br>o 100 100 98<br>2 94 94 93<br>88 88<br>79 82<br>76 76<br>ite)x<br>a<br>F<br>353<br>rwa)<br>S<br>se 36 35<br>lo<br>N 14<br>fo)<br><=5km 5-10km 10-15km >15km<br>(GEG Piped water +=) improved water<br>| Basic water {| Improved water on premises<br><!-- End of picture text -->

<!-- Start of picture text --> Distance from City Core<br>S 98<br>S<br>x<br>66<br>g<br>2<br>FABao 52<br>ro<br>Sx 41<br>29<br>“Nre} 7 24<br>14<br>o<br><=5km 5-10km 10-15km >15km<br>(| Limited and basic sanitation hl Basic sanitation<br><!-- End of picture text -->

<!-- Start of picture text --> Distance from City Core<br>o<br>Ss<br>= 92<br>75<br>lo 80<br>‘ 67<br>32[=]g 62<br>28<br>x} 39<br>ro 35<br>re}<br>“N<br>"<br>7<br>4 2<br>°<br><=5km 5-10km 10-15km >15km<br>GEG swmsevice =) Electricity<br>(ER The Internet<br><!-- End of picture text -->
<!-- Start of picture text --> Precarious/non-precarious<br>8= 95 97<br>88<br>81 78<br>3rewo 1<br>2gBa 53<br>25<br>s<br>py<br>re) 25<br>N<br>oO<br>Precarious Non-precarious<br>(ER Piped water ~=)- Improved water<br>(HE Basic water = (EI) Improved water on premises<br><!-- End of picture text --> <!-- Start of picture text --> Precarious/non-precarious<br>8=<br>re 70<br>3<br>2gFa<br>28 46<br>SsPS<br>28<br>wo<br>N 17<br>o<br>Precarious Non-precarious<br>(EE) Limited and basic sanitation (I) Basic sanitation<br><!-- End of picture text --> <!-- Start of picture text --> Precarious/non-precarious<br>fo)<br>S<br>=<br>79<br>ite)<br>~<br>3<br>32 58 60<br>3 fo)<br>=6 42<br>co)<br>=<br>ire)<br>N<br>9<br>4<br>°o<br>Precarious Non-precarious<br>GE) swMsenice [ Electricity<br>(EA The Internet<br><!-- End of picture text -->
<!-- Start of picture text --> Strate<br>° 96 99 100 97 100100<br>S 88 92 «= 93 9. 93 95 pg<br>1 7 73 7<br>s<br>3Alo} 63 55<br>S 50<br>Fs237 “4 42<br>z 34<br>36 27<br>1N 15<br>6 8<br>0<br>oO P low oly no NP ue NP Rural<br>medium igh ow medium high<br>(ER Piped water ~=)- Improved water<br>(HE Basic water = (EI) Improved water on premises<br><!-- End of picture text --> <!-- Start of picture text --> Strate<br>°<br>S 96<br>77<br>Xx<br>a 64<br>3 59<br>o<br>88zo 46<br>3<br># 7<br>27<br>a 19 7 2 20<br>12<br>o 0 0<br>P low P P NP NP NP Rural<br>medium high low medium high<br>(EE) Limited and basic sanitation (I) Basic sanitation<br><!-- End of picture text --> <!-- Start of picture text --> Strate<br>°<br>Ss=<br>89<br>86<br>79<br>rR<br>67 67 66<br>2<br>iret23 60 60<br>io)<br>rw 44 44<br>S<br>x 35<br>28<br>Ye)<br>N<br>° 7 10<br>6 4<br>2 2 4 9<br>° P low Pp Pp NP NP NP Rural<br>medium high low medium high<br>(GE) swMsernvice =) Electricity<br>(EN The Internet<br><!-- End of picture text -->
<!-- Start of picture text --> Bw<br>a 2<br>Bo 19<br>5a<br>o<br>Q 17<br>Bio 15<br>ge<br>2<br>2<br>ae<br>=<br>SoS 6 4 5<br>P low P medium P high NP low NP medium NP high Rural<br><!-- End of picture text --> <!-- Start of picture text --> a<br>‘ ie ede aa se “is, | Legend<br>x \ Ee the ts Project sites<br>df Le of 4 AT ‘|<br>a a i ce + cBD<br>« Sw ak ian eg m A 5km<br>oe ba . x t 10km<br>Sy . x | ars 415km<br>‘ a = ane — é Flood Risk (Next 100 Years<br>; : y : 1.000 - 0.071<br>q 0.072 - 0.229<br>2 | Ml 0.230 - 0.486<br>he,_ aan<MHB 0.4870.986-- 3.2000.985<br>“ . r 4 ~<br>3 i NSO ra<br><!-- End of picture text -->
services—thereby increasing the overall density of those areas—even though this comes at the cost of paying higher prices/rents for housing.

As expected, access to network-dependent infrastructure, such as piped water, is more closely linked to distance from the city core and built-up density. While density is positively correlated with the chance of households’ access to improved water and basic water, the linkage with piped water is stronger. This is because access to piped water is predicated on the networks of main water pipelines and the development of the main and local pipelines may be less costly in dense areas. For the same reason, the distance from the city core is also most strongly (negatively) correlated with households’ chance of having access to piped water. In case of sanitation access, there is no clear relationship observed between density and access to basic sanitation, reflecting that use of pit latrines are location specific.

The benefits of agglomeration, however, are not uniform across different segments of the population. In particular, living in highly dense neighborhoods improves the odds of having access to quality water services (for example, piped/improved/basic water) particularly for the poorest households (for example, the bottom 20 percentile of consumption) while richer households stand to benefit more in terms of access to some of the other services such as limited/basic sanitation or SWM services.<sup>13</sup> Even if it is preferable to live in dense and/or well-connected neighborhoods, those poor households may not necessarily be able to afford those amenities. One of the potential reasons for this pattern may be that rents for neighborhoods with access to these services tend to be more expensive, which in turn deter poor households from living in those areas.

Households with higher levels of education stand to benefit from greater access to core public services although these education dividends are not uniform across different age groups. Even after accounting for various spatial and demographic characteristics, education attainment is positively correlated with access to high-quality services such as improved water on premises, basic sanitation, SWM services, and electricity. Education dividends are particularly evident for a relatively small fraction of households whose heads had completed university degrees. This finding does not come as a surprise given that those highly educated individuals tend to concentrate in neighborhoods that are proximate to the city core and have greater access to quality public services. That being said, these education dividends do not accrue to all age groups. More specifically, households headed by young working-age individuals (15–24 years) do not seem to enjoy greater access to services even if they have higher levels of education.<sup>14</sup>

# **4.2 Costs of living in housing and neighborhoods with better access to services**

An obvious reason why low-income residents have low level (or quality) of access to services is that they cannot afford such services. However, another equally important reason is that they cannot live in neighborhoods that are well connected to basic services and other amenities. In functioning housing markets, housing values are expected to reflect the level and quality of access to amenities. Housing rents are clearly lower in precarious neighborhoods and/or peri-urban areas (Figure 20). By allocating 20 percent of its monthly budget for housing, a household with its monthly consumption expenditures equivalent to the median value of Kinshasa households can

> 13 These are indicated by negative interaction effects between wealth and building density in Table A.1.

> 14 These are indicated by negative interaction effects between education levels and youth dummy variable in Table A.1.

25
mostly afford housing units in precarious neighborhoods. Also, there is a stark contrast in housing rents, depending on the quality and access to services (Figure 21). Given the limited budget that can be allocated for housing, low-income households need to determine their residences by weighing the costs and benefits based on their preferences.

**Figure 20: Housing rents by locations**

<!-- Start of picture text --> (A) By precariousness (B) By precariousness and density<br>0 200 400 600 800<br>Monthly housing rent (CDF in thousands)<br>Precarious Non-precarious<br>excludes outside values<br>P low P medium P high NP low NP medium NP high<br>.015 800<br>600<br>.01<br>400<br>Kernel density<br>.005<br>200<br>Monthly housing rent (CDF in thousands) 0<br>0<br><!-- End of picture text --> _Source:_ Authors’ calculations based on the Kinshasa Survey. _Note_ : Seven different strata as defined in the Kinshasa Survey are (a) low-density precarious areas (P low), (b) medium-density precarious areas (P medium), (c) high-density precarious areas (P high), (d) low-density nonprecarious areas (NP low), (e) medium-density non-precarious areas (NP medium), and (f) high-density nonprecarious areas (NP high). Red line indicates the median monthly consumption value (CDF 83,000).

26
<!-- Start of picture text --> —2gs<br>g<br>£s<br>©<br>a<br>Oo<br>=3<br>2<br>=232fo}<br>2 8<br>=Ss<br>aa =<br>S ~ ye ><br>& Ss e F<br>BS) a) & &<br>sfe gs<br>.<br>excludes outside values<br><!-- End of picture text --> <!-- Start of picture text -->
[=]g<br>a<br>2<br>g sn<br>£<br>c<br>KB<br>o~<br>=<br>—o_<br>28<br>a<br>3<br>5=<br>£8<br>S<br>=<br>°<br>excludes outsideNo piped values water Piped water<br><!-- End of picture text --> <!-- Start of picture text --> °<br>S<br>wo<br>3_Cc<br>88<br>So<br>ov<br>r==<br><<br>rs)La 3<br>2<br><<br>2<br>oS<br>BS<br>Ss<br>g<br>ags><br>5.<br>o<br>excludesNo improvedoutside water values on premises Improved waster on premises<br><!-- End of picture text --> <!-- Start of picture text --> So<br>Ss<br>+<br>~<br>3<4<br>5<br>So<br>ag<br>=<br>£<br>rea[S)<br>=8<br>SN<br>2<br>2<br>&<br>Sa<br>Zo<br>2c2> Co<br>S<br>oO<br>excludesa No limitedbasicoutside values sanitation Limited/basic sanitation<br><!-- End of picture text -->
rents tend to be higher in locations closer to the city core. More specifically, housing rents become on average 11 percent lower every 1 km away from the city core. This negative correlation between the distance from the city core and housing rents remains statistically significant even after controlling for the quality of dwelling characteristics and access to services (4 percent lower rents with every 1 km away from the CBD). This suggests that given the characteristics of dwelling and access to services, it still costs extra to live closer to the city core, where economic opportunities and urban amenities are concentrated.

Moreover, it is less expensive to live in neighborhoods that are precarious and/or are prone to flooding (even after controlling for dwelling and service quality), therefore attracting low-income households. As already seen, housing rents are clearly lower in precarious areas. This is partly because those housing units tend to be structured with low-quality materials and lack access to services. Even after discounting such low-quality aspects, housing rents are on average 20 percent lower in precarious areas, compared to non-precarious areas. There may be some other negative characteristics (that is, disamenities) in precarious neighborhoods. Nevertheless, such disamenities lower the rents in precarious areas and thereby attract low-income households. Similar discount in rents is found in neighborhoods that are prone to flooding (11 percent lower rents).

# **5. Conclusion**

Drawing on recently collected household survey data, this paper examines living conditions in Kinshasa, by focusing on its spatial variations. The key findings are summarized as follows. First, worse living conditions are endured by poor and/or less educated households in precarious and/or peri-urban neighborhoods. Second, inequality in access to water is driven by locational factors, while sanitation access depends on household characteristics, such as the education levels of household heads. Third, while there is a concern about negative externalities from Kinshasa’s extremely high population and built-up density, poor households benefit from density to some extent, as indicated by a higher chance of their gaining access to basic services in dense neighborhoods. Finally, high housing costs in neighborhoods closer to the city core restrict access to economic opportunities and services among low-income households.

It is imperative to increase the supply of affordable housing to lessen the inequality of access to services in Kinshasa. Without providing affordable housing in the core urban areas of Kinshasa, the poor will continue to be excluded from the service benefits of city agglomeration —a tighter network of public services that more densely populated areas offer, such as public taps, public toilets, or SWM services that are less frequently seen in more sparsely populated peri-urban areas. At the same time, it is imperative that targeted investments are made in key peri-urban areas to reduce such spatial inequalities and improve access to basic public services for the poor households that seem to concentrate in those peri-urban or rural neighborhoods.

The proliferation of precarious neighborhoods in the extremely dense and populous city makes its residents potentially vulnerable to the COVID-19 pandemic. While density brings various benefits, it creates serious problems if not managed well. The risk of the spread of COVID-19 is very high in neighborhoods characterized by high population density, overcrowded housing, inadequate access to water, sanitation, and SWM services, and frequent exposure to flooding. As shown in this note, many precarious neighborhoods in Kinshasa have such characteristics. Spatially targeted preventive interventions that reduce such risk are critically important.

28
# **Annex A: Regression tables**

**Table A.1: Regression analysis on the determinants of access to services**

||**Piped water**|**Improved**<br>**water **|**Basic water**|**Improved water**<br>**on premises**|
|---|---|---|---|---|
|Age|0.002|−0.000|−0.000|0.003|
||(0.002)|(0.001)|(0.001)|(0.002)*|
|Female|<br>−0.025|<br>0.023|<br>−0.050|<br>0.019|
||(0.039)|(0.018)|(0.031)|(0.040)|
|1 dependent per working-age person|−0.047|−0.054|−0.046|0.055|
||(0.041)|(0.034)|(0.043)|(0.053)|
|1−2 dependents per working-age person|−0.110|−0.025|−0.053|0.043|
||(0.068)|(0.024)|(0.039)|(0.061)|
|> 2 dependents per working-age person|−0.115|−0.075|−0.158|0.053|
||(0.085)|(0.075)|(0.093)*|(0.065)|
|Secondary|<br>0.078|<br>−0.028|<br>0.076|<br>0.057|
||(0.054)|(0.033)|(0.093)|(0.033)*|
|University|<br>0.051|<br>−0.063|<br>0.024|<br>0.198|
||(0.061)|(0.036)*|(0.074)|(0.050)***|
|Distance from CBD|<br>−0.078|<br>−0.009|<br>−0.028|<br>−0.019|
||(0.013)***|(0.008)|(0.014)**|(0.015)|
|Distance from CBD (squared)|<br>0.003|<br>0.001|<br>0.001|<br>0.001|
||(0.000)***|(0.000)*|(0.000)***|(0.000)*|
|Young working age 15−24|0.564|−0.015|0.229|0.146|
||(0.170)***|(0.104)|(0.106)**|(0.068)**|
|Young working age 15−24|−0.586|−0.021|−0.213|−0.133|
|×Secondary|(0.173)***|(0.113)|(0.122)*|(0.130)|
|Young working age 15−24|−0.396|0.036|−0.329|−0.437|
|×University|(0.205)*|(0.118)|(0.200)|(0.164)***|
|Consumption quintile (20–40 percentile)|0.486|0.482|0.569|−0.194|
||(0.229)**|(0.212)**|(0.251)**|(0.172)|
|Consumption quintile (40–60 percentile)|<br>0.394|<br>0.278|<br>0.589|<br>−0.158|
||(0.261)|(0.217)|(0.293)**|(0.151)|
|Consumption quintile (60–80 percentile)|<br>1.030|<br>0.826|<br>1.069|<br>0.002|
||(0.310)***|(0.222)***|(0.381)***|(0.307)|
|Consumption quintile (80–100 percentile)|<br>1.575|<br>0.877|<br>0.790|<br>0.092|
|<br>(The richest)|(0.377)***|(0.341)**|(0.527)|(0.458)|
|<br>Building density (log transformed)|<br>0.343|<br>0.160|<br>0.278|<br>0.096|
||(0.038)***|(0.046)***|(0.058)***|(0.031)***|
|Building density (log transformed)|<br>−0.057|<br>−0.063|<br>−0.074|<br>0.035|
|×Consumption quintile (20–40 percentile)|(0.030)*|(0.026)**|(0.032)**|(0.027)|
|Building density (log transformed)|−0.038|−0.035|−0.077|0.027|
|×Consumption quintile (40–60 percentile)|(0.032)|(0.027)|(0.037)**|(0.023)|
|Building density (log transformed)|−0.116|−0.101|−0.140|0.019|
|<br>×Consumption quintile (60–80 percentile)|(0.039)***|(0.027)***|(0.048)***|(0.042)|
|<br>Building density (log transformed)|<br>−0.184|<br>−0.110|<br>−0.095|<br>0.027|
|×Consumption quintile (80–100 percentile)|(0.050)***|(0.040)***|(0.065)|(0.056)|
|<br>Precarious|<br>−0.071|<br>0.040|<br>0.060|<br>0.187|
||(0.082)|(0.027)|(0.063)|(0.067)***|
|Flood risk - high|0.026|0.010|0.113|−0.032|
||(0.075)|(0.026)|(0.046)**|(0.048)|
|Adtd Rd|039|018|022|023|
|juse -square|.|.|.|.|
|Observation|1,511|1,511|1,512|1,525|

_Note:_ Standard errors in parentheses and clustered by primary sampling units.<sup>*</sup> p < 0.1,<sup>**</sup> p < 0.05,<sup>***</sup> p < 0.01.

29
**Table A.1 (continued): Regression analysis on the determinants of access to services**

||**Limited/basic**<br>**sanitation**|**Basic**<br>**sanitation**|**SWM**|**Electricity**|**Internet**|
|---|---|---|---|---|---|
|Age|0.004|0.006|0.001|−0.002|0.001|
||(0.002)**|(0.001)***|(0.002)|(0.002)|(0.001)|
|Female|−0.014|0.017|−0.080|−0.007|0.021|
||(0.042)|(0.032)|(0.069)|(0.054)|(0.018)|
|1 dependent per working-age person|0.024|0.072|0.007|−0.046|−0.002|
||(0.060)|(0.052)|(0.061)|(0.060)|(0.035)|
|1−2 dependents per working-age person|−0.010|0.083|−0.035|0.003|−0.006|
||(0.069)|(0.039)**|(0.064)|(0.049)|(0.031)|
|> 2 dependents per working-age person|<br>−0.012|<br>0.047|<br>0.100|<br>0.013|<br>−0.000|
||(0.065)|(0.057)|(0.111)|(0.065)|(0.033)|
|Secondary|<br>0.079|<br>0.080|<br>0.056|<br>0.119|<br>0.005|
||(0.056)|(0.036)**|(0.049)|(0.069)*|(0.019)|
|University|<br>0.268|<br>0.321|<br>0.154|<br>0.198|<br>0.020|
||(0.074)***|(0.067)***|(0.078)**|(0.089)**|(0.017)|
|Distance from CBD|<br>−0.048|<br>0.009|<br>−0.050|<br>−0.079|<br>−0.007|
||(0.011)***|(0.010)|(0.020)**|(0.016)***|(0.004)**|
|Distance from CBD (squared)|<br>0.002|<br>−0.000|<br>0.001|<br>0.002|<br>0.000|
||(0.000)***|(0.000)|(0.001)***|(0.001)***|(0.000)*|
|Young working age 15−24|0.117|0.217|−0.130|−0.249|0.001|
||(0.118)|(0.100)**|(0.194)|(0.238)|(0.045)|
|Young working age 15−24|0.091|−0.210|0.152|−0.037|−0.023|
|<br>×Secondary|(0.161)|(0.105)**|(0.265)|(0.271)|(0.041)|
|<br>Young working age 15−24|<br>−0.610|<br>−0.353|<br>−0.064|<br>−0.231|<br>−0.094|
|×University|(0.201)***|(0.189)*|(0.302)|(0.284)|(0.052)*|
|Consumption quintile (20–40 percentile)|−0.601|−0.141|−0.869|0.234|−0.015|
||(0.235)**|(0.121)|(0.330)***|(0.279)|(0.052)|
|Consumption quintile (40–60 percentile)|<br>−0.457|<br>0.069|<br>−0.815|<br>0.155|<br>−0.024|
||(0.255)*|(0.169)|(0.399)**|(0.338)|(0.032)|
|Consumption quintile (60–80 percentile)|<br>−0.208|<br>0.422|<br>−1.136|<br>0.414|<br>−0.119|
||(0.228)|(0.227)*|(0.337)***|(0.399)|(0.108)|
|Consumption quintile (80–100 percentile)|<br>0.111|<br>0.864|<br>−0.277|<br>0.862|<br>0.151|
|<br>[The richest]|(0.475)|(0.399)**|(0.605)|(0.558)|(0.261)|
|Building density (log transformed)|<br>0.125|<br>−0.015|<br>0.031|<br>0.094|<br>0.007|
||(0.034)***|(0.018)|(0.052)|(0.062)|(0.009)|
|Building density (log transformed)|<br>0.100|<br>0.025|<br>0.106|<br>−0.026|<br>0.005|
|×Consumption quintile (20−40 percentile)|(0.034)***|(0.016)|(0.043)**|(0.039)|(0.007)|
|Building density (log transformed)|0.095|0.012|0.098|−0.006|0.002|
|×Consumption quintile (40−60 percentile)|(0.036)***|(0.022)|(0.049)**|(0.042)|(0.004)|
|Building density (log transformed)|0.077|−0.032|0.148|−0.040|0.019|
|<br>×Consumption quintile (60−80 percentile)|(0.030)**|(0.028)|(0.040)***|(0.043)|(0.015)|
|<br>Building density (log transformed)|<br>0.047|<br>−0.076|<br>0.048|<br>−0.094|<br>−0.009|
|×Consumption quintile (80−100 percentile)|(0.056)|(0.048)|(0.074)|(0.060)|(0.032)|
|<br>Precarious|<br>0.072|<br>0.018|<br>0.028|<br>−0.016|<br>0.020|
||(0.064)|(0.056)|(0.088)|(0.071)|(0.022)|
|Flood risk - high|−0.081|0.013|−0.001|0.053|0.017|
||(0.072)|(0.102)|(0.094)|(0.090)|(0.034)|
|Adtd Rd|038|023|016|026|005|
|juse -square|.|.|.|.|.|
|Observation|1,509|1,509|1,450|1,526|1,526|

_Note:_ Standard errors in parentheses and clustered by primary sampling units.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

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**Table A.2 : Regression analysis on the determinants of education levels**

|Dependent variable|Preschool/<br>primary|Secondary|Tertiary|Preschool<br>/primary|Secondary|Tertiary|
|---|---|---|---|---|---|---|
|Distance|0.004|0.006|-0.010|0.002|0.003|-0.005|
|from CBD|(0.005)|(0.006)|(0.005)**|(0.004)|(0.005)|(0.004)|
|Building density|-0.010|0.017|-0.007|-0.018|0.034|-0.017|
|(log transformed)|(0.023)|(0.027)|(0.024)|(0.022)|(0.024)|(0.019)|
|Precarious|-0.019|-0.098|0.118|-0.005|-0.022|0.027|
||(0.037)|(0.049)**|(0.062)*|(0.038)|(0.044)|(0.054)|
|Flood risk - high|0.068|-0.080|0.012|0.010|-0.040|0.030|
||(0.059)|(0.076)|(0.048)|(0.074)|(0.075)|(0.057)|
|Age||||0.003|-0.008|0.005|
|||||(0.001)***|(0.002)***|(0.002)***|
|Female||||-0.171|-0.013|0.184|
|||||(0.033)***|(0.039)|(0.035)***|
|1 dependent per||||-0.011|0.142|-0.131|
|working-age person||||(0.024)|(0.063)**|(0.067)*|
|1−2 dependents per||||0.009|0.160|-0.169|
|working-age person||||(0.040)|(0.076)**|(0.080)**|
|> 2 dependents per||||0.084|0.141|-0.225|
|working-age person||||(0.082)|(0.108)|(0.076)***|
|Consumption quintile||||-0.023|-0.114|0.136|
|(20–40 percentile)||||(0.056)|(0.071)|(0.044)***|
|Consumption quintile||||-0.072|-0.071|0.144|
|(40–60 percentile)||||(0.063)|(0.070)|(0.054)***|
|Consumption quintile||||-0.077|-0.138|0.214|
|(60–80 percentile)||||(0.076)|(0.064)**|(0.052)***|
|Consumption quintile||||-0.100|-0.327|0.427|
|(80–100 percentile)<br>(Richest)||||(0.057)*|(0.068)***|(0.059)***|
|Adjusted R-squared|0.01|0.01|0.03|0.10|0.12|0.20|
|Observation|1,792|1,792|1,792|1,526|1,526|1,526|

_Note:_ Standard errors in parentheses and clustered by primary sampling units. Each of these regressions conducts a linear probability model testing the effects of each covariate on the likelihood that household heads have preschool/primary (the first and fourth columns), secondary (the second and fifth columns), and tertiary education (the third and sixth columns).<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

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**Table A.3 : Hedonic regression results**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Score=0 (worst) [reference]|||||
|Score=1|0.505<sup>***</sup><br>|0.472<sup>***</sup><br>|0.479<sup>***</sup><br>|0.476<sup>***</sup><br>|
||(0.115)|(0.095)|(0.093)|(0.095)|
|Score=2|0.939<sup>***</sup>|0.695<sup>***</sup>|0.715<sup>***</sup>|0.701<sup>***</sup>|
||(0.118)|(0.098)|(0.097)|(0.098)|
|Score=3 (best)|1.201<sup>***</sup>|0.804<sup>***</sup>|0.824<sup>***</sup>|0.807<sup>***</sup>|
||(0.143)|(0.120)|(0.118)|(0.119)|
|Tap inside dwelling [reference]|||||
|<br>Tap outside dwelling||−0.021|−0.003|−0.019|
|||(0.068)|(0.067)|(0.068)|
|Public tap||−0.404<sup>***</sup>|−0.339<sup>***</sup>|−0.402<sup>***</sup>|
|||(0.120)|(0.118)|(0.120)|
|Shared tap||−0.255<sup>***</sup>|−0.214<sup>***</sup>|−0.253<sup>***</sup>|
|||(0.073)|(0.072)|(0.073)|
|Other improved water||−0.471<sup>***</sup>|−0.461<sup>***</sup>|−0.476<sup>***</sup>|
|||(0.080)|(0.079)|(0.080)|
|Unimproved water||<br>−0.604<sup>***</sup>|<br>−0.548<sup>***</sup>|<br>−0.612<sup>***</sup>|
|||(0.124)|(0.122)|(0.124)|
|Flush toilet inside dwelling [reference]|||||
|<br>Flush toilet outside dwelling||−0.360<sup>***</sup>|−0.376<sup>***</sup>|−0.364<sup>***</sup>|
|||(0.078)|(0.077)|(0.078)|
|Pit latrine||−0.496<sup>***</sup>|−0.493<sup>***</sup>|−0.494<sup>***</sup>|
|||(0.066)|(0.065)|(0.066)|
|Unimproved sanitation||−0.880<sup>***</sup>|−0.867<sup>***</sup>|−0.875<sup>***</sup>|
|||(0.079)|(0.078)|(0.079)|
|SWM service||0.169<sup>***</sup>|0.161<sup>***</sup>|0.156<sup>***</sup>|
|||(0.038)|(0.038)|(0.039)|
|Electricity||0.226<sup>***</sup>|0.206<sup>***</sup>|0.228<sup>***</sup>|
|||(0.046)|(0.046)|(0.046)|
|Distance from CBD|−0.122<sup>***</sup>|−0.043<sup>***</sup>|−0.024<sup>***</sup>|−0.046<sup>***</sup>|
||(0.008)|(0.008)|(0.008)|(0.008)|
|Distance from CBD (squared)|0.002<sup>***</sup>|0.000<sup>**</sup>|−0.000|0.000<sup>**</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Precarious|||<br>−0.225<sup>***</sup><br>(0.038)||
|Flood risk - high||||−0.123<sup>**</sup>|
|||||(0.059)|
|Adjusted R-squared|0.693|0.798|0.804|<br>0.798|
|<br>Observation<br>|1,082<br>|1,018<br>|1,018<br>|1,018<br>|
|_Note:_Standard errors in parentheses.<sup>*</sup> _p_< 0.1<br>rooms.|,<sup>**</sup> _p_< 0.05,<sup>***</sup> _p_|< 0.01. Additiona|l controls include t|he number of|

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