Population living in slums 2025
This statistic measures the percentage of a country's population living in slums, highlighting urban poverty and living conditions. Understanding slum populations is crucial for addressing housing challenges and improving the quality of life.
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Complete Data Rankings
Rank | ||
|---|---|---|
1 | South Sudan | 94.2 % |
2 | Mali | 85.537 % |
3 | Chad | 81.069 % |
4 | Congo, Democratic Republic of the | 79.629 % |
5 | Sao Tome and Principe | 78.416 % |
6 | Afghanistan | 73.742 % |
7 | Burkina Faso | 72.843 % |
8 | Sudan | 71.181 % |
9 | Jordan | 70.65 % |
10 | Niger | 70.444 % |
11 | Angola | 69.75 % |
12 | Central African Republic | 68.913 % |
13 | Congo | 67.085 % |
14 | Equatorial Guinea | 66.144 % |
15 | Benin | 63.774 % |
16 | Madagascar | 62.045 % |
17 | Myanmar | 61.972 % |
18 | Tanzania | 61.311 % |
19 | Ethiopia | 60.755 % |
20 | Liberia | 58.765 % |
21 | Ecuador | 57.874 % |
22 | Mauritania | 55.524 % |
23 | Pakistan | 53.534 % |
24 | Guinea-Bissau | 53.406 % |
25 | Iraq | 51.81 % |
26 | Zimbabwe | 50.599 % |
27 | Bangladesh | 50.514 % |
28 | Comoros | 50.492 % |
29 | Mozambique | 49.337 % |
30 | Haiti | 49.057 % |
31 | Uganda | 48.512 % |
32 | Côte d'Ivoire | 46.984 % |
33 | Laos | 46.816 % |
34 | Bolivia | 45.889 % |
35 | Guinea | 45.797 % |
36 | Zambia | 45.774 % |
37 | Tuvalu | 45.73 % |
38 | Sierra Leone | 45.035 % |
39 | Djibouti | 44.766 % |
40 | Eritrea | 44.766 % |
41 | Mauritius | 44.766 % |
42 | Seychelles | 44.766 % |
43 | Somalia | 44.766 % |
44 | Nigeria | 44.526 % |
45 | Bhutan | 44.276 % |
46 | Iran | 44.276 % |
47 | Sri Lanka | 44.276 % |
48 | Yemen | 43.569 % |
49 | Peru | 43.042 % |
50 | Cabo Verde | 42.603 % |
51 | Namibia | 41.349 % |
52 | Kenya | 40.754 % |
53 | Syrian Arab Republic | 40.463 % |
54 | Gabon | 39.329 % |
55 | Senegal | 39.243 % |
56 | Cambodia | 39.163 % |
57 | Botswana | 37.164 % |
58 | Nepal | 36.752 % |
59 | Guatemala | 36.48 % |
60 | Malawi | 35.114 % |
61 | Gambia | 34.378 % |
62 | Togo | 34.023 % |
63 | Philippines | 34.015 % |
64 | Rwanda | 33.563 % |
65 | Maldives | 33.312 % |
66 | Ghana | 31.807 % |
67 | Samoa | 30.664 % |
68 | Burundi | 29.372 % |
69 | Timor-Leste | 29.268 % |
70 | Cameroon | 27.441 % |
71 | Vietnam | 27.355 % |
72 | Honduras | 26.427 % |
73 | China | 26.321 % |
74 | Venezuela | 25.216 % |
75 | South Africa | 23.77 % |
76 | North Korea | 23.036 % |
77 | Papua New Guinea | 22.937 % |
78 | Brunei Darussalam | 19.694 % |
79 | Lesotho | 18.944 % |
80 | Libya | 18.512 % |
81 | Suriname | 17.14 % |
82 | Indonesia | 17.136 % |
83 | State of Palestine | 16.326 % |
84 | Mexico | 16.195 % |
85 | Belize | 15.719 % |
86 | Argentina | 14.15 % |
87 | Turkey | 13.778 % |
88 | Brazil | 13.649 % |
89 | El Salvador | 13.493 % |
90 | Panama | 13.116 % |
91 | Eswatini | 12.674 % |
92 | Cuba | 12.669 % |
93 | Paraguay | 11.675 % |
94 | Mongolia | 11.523 % |
95 | Dominican Republic | 10.067 % |
96 | Morocco | 9.457 % |
97 | Fiji | 8.9 % |
98 | Ireland | 8.78 % |
99 | Guyana | 8.752 % |
100 | Colombia | 8.559 % |
101 | Trinidad and Tobago | 8.181 % |
102 | Turkmenistan | 7.994 % |
103 | Armenia | 7.832 % |
104 | Algeria | 6.999 % |
105 | Montenegro | 6.852 % |
106 | Tunisia | 6.843 % |
107 | Chile | 6.506 % |
108 | Poland | 5.991 % |
109 | Georgia | 5.852 % |
110 | Cayman Islands | 5.567 % |
111 | India | 5.41 % |
112 | South Korea | 5.27 % |
113 | Azerbaijan | 5.015 % |
114 | Lebanon | 4.528 % |
115 | Nicaragua | 4.086 % |
116 | Kiribati | 3.901 % |
117 | Uzbekistan | 3.832 % |
118 | Vanuatu | 3.255 % |
119 | Hungary | 2.965 % |
120 | Marshall Islands | 2.936 % |
121 | Sint Maarten (Dutch part) | 2.888 % |
122 | Saint Vincent and the Grenadines | 2.755 % |
123 | Antigua and Barbuda | 2.646 % |
124 | Russia | 2.504 % |
125 | Egypt | 2.168 % |
126 | Costa Rica | 2.082 % |
127 | Japan | 2 % |
128 | Thailand | 2 % |
129 | Romania | 1.976 % |
130 | French Polynesia | 1.5 % |
131 | Solomon Islands | 1.342 % |
132 | Serbia | 1.254 % |
133 | Canada | 1.156 % |
134 | United States Virgin Islands | 1.096 % |
135 | Jamaica | 0.884 % |
136 | Nauru | 0.868 % |
137 | Turks and Caicos Islands | 0.631 % |
138 | Guam | 0.552 % |
139 | Saudi Arabia | 0.55 % |
140 | Slovenia | 0.527 % |
141 | Croatia | 0.474 % |
142 | Palau | 0.396 % |
143 | Latvia | 0.339 % |
144 | Estonia | 0.321 % |
145 | Cyprus | 0.3 % |
146 | Sweden | 0.288 % |
147 | Republic of Moldova | 0.265 % |
148 | New Caledonia | 0.25 % |
149 | Bulgaria | 0.224 % |
150 | Malaysia | 0.2 % |
151 | Northern Mariana Islands | 0.2 % |
152 | United Kingdom | 0.156 % |
153 | Bermuda | 0.08 % |
154 | North Macedonia | 0.068 % |
155 | United States | 0.066 % |
156 | Slovakia | 0.064 % |
157 | Tajikistan | 0.05 % |
158 | Spain | 0.045 % |
159 | Malta | 0.038 % |
160 | Australia | 0.03 % |
161 | Czech Republic | 0.03 % |
162 | Italy | 0.02 % |
163 | Luxembourg | 0.019 % |
164 | United Arab Emirates | 0.009 % |
165 | Belarus | 0.004 % |
166 | Tonga | 0.002 % |
167 | Portugal | 0.001 % |
168 | Andorra | 0 % |
169 | Aruba | 0 % |
170 | Bahrain | 0 % |
171 | Belgium | 0 % |
172 | Denmark | 0 % |
173 | Finland | 0 % |
174 | France | 0 % |
175 | Germany | 0 % |
176 | Iceland | 0 % |
177 | Kuwait | 0 % |
178 | Monaco | 0 % |
179 | Netherlands | 0 % |
180 | New Zealand | 0 % |
181 | Norway | 0 % |
182 | Qatar | 0 % |
183 | Singapore | 0 % |
184 | Switzerland | 0 % |
185 | Lithuania | -0.002 % |
186 | Saint Lucia | -0.223 % |
187 | Oman | -0.265 % |
188 | Greece | -0.275 % |
189 | Austria | -0.391 % |
190 | Ukraine | -0.405 % |
191 | Uruguay | -0.574 % |
192 | British Virgin Islands | -0.841 % |
193 | Albania | -1.29 % |
194 | Bosnia and Herzegovina | -1.346 % |
195 | Kazakhstan | -1.503 % |
196 | Kyrgyzstan | -4.716 % |
- #1
South Sudan
- #2
Mali
- #3
Chad
- #4
Congo, Democratic Republic of the
- #5
Sao Tome and Principe
- #6
Afghanistan
- #7
Burkina Faso
- #8
Sudan
- #9
Jordan
- #10
Niger
Analysis: These countries represent the highest values in this dataset, showcasing significant scale and impact on global statistics.
- #196
Kyrgyzstan
- #195
Kazakhstan
- #194
Bosnia and Herzegovina
- #193
Albania
- #192
British Virgin Islands
- #191
Uruguay
- #190
Ukraine
- #189
Austria
- #188
Greece
- #187
Oman
Context: These countries or territories have the lowest values, often due to geographic size, administrative status, or specific characteristics.
Analysis & Context
In 2025, South Sudan leads the world with the highest Population living in slums at 94.2%, while the global range spans from -4.72% to 94.20%. The global average percentage of populations living in slums is 21.25%, providing a critical context for understanding urban poverty and living conditions worldwide.
Economic and Political Drivers of High Slum Populations
The prevalence of slum populations in countries like South Sudan (94.2%), Mali (85.54%), and Chad (81.07%) can often be attributed to a combination of economic instability and political challenges. These nations frequently experience ongoing conflicts and economic hardships, which severely limit their ability to provide adequate housing and infrastructure. For instance, the ongoing civil unrest in South Sudan has displaced millions, exacerbating urban poverty and leading to high slum percentages.
Moreover, countries like Afghanistan (73.74%) and the Democratic Republic of the Congo (79.63%) face similar issues where prolonged conflict and weak governance hinder urban development. These conditions make it difficult to implement effective housing policies, leading to a significant portion of the population living in informal settlements.
Geographic and Environmental Influences
Geographic and environmental factors also play a role in the distribution of slum populations. In Burkina Faso (72.84%) and Sao Tome and Principe (78.42%), rapid urbanization without corresponding infrastructure development contributes to the expansion of slum areas. These countries often face geographical constraints, such as limited arable land and susceptibility to natural disasters, which can force populations into densely packed urban areas lacking proper sanitation and housing.
Similarly, Niger (70.44%) and Sudan (71.18%) experience harsh environmental conditions that drive rural populations into urban centers, where they encounter inadequate urban planning and insufficient housing options.
Understanding the Anomalous Negative Values
The data reveals some intriguing negative values for slum populations in countries like Kyrgyzstan (-4.72%) and Kazakhstan (-1.50%). These anomalies may reflect data reporting inconsistencies or methodological issues in capturing slum statistics rather than actual reductions to below zero percentages. Nevertheless, these figures highlight the importance of robust data collection and analysis methods in understanding the true scope of urban poverty.
In regions like Bosnia and Herzegovina (-1.35%) and Albania (-1.29%), historical shifts from centrally planned economies to more market-oriented systems might explain improved urban living conditions, yet the negative values warrant further investigation into data accuracy and reporting standards.
Year-over-Year Movements and Emerging Trends
The year-over-year data for 2025 shows significant movements, with Jordan experiencing the largest increase of 5.25% in slum populations, reaching 70.65%. This increase may be linked to the influx of refugees and economic migrants, straining urban infrastructure and housing availability. Similarly, Mali saw a rise of 4.25% to 85.54%, reflecting ongoing challenges in urban management and economic development.
Conversely, countries like Malawi saw a decrease of 2.06% in slum populations, potentially due to targeted interventions and international aid aimed at improving urban infrastructure and housing. This trend suggests that sustained policy efforts can lead to measurable improvements in living conditions, although progress remains slow and uneven.
Overall, the 2025 statistics on the Population living in slums underscore the complex interplay of economic, political, and environmental factors that drive urban poverty. The data highlights the urgent need for comprehensive strategies to address housing shortages and improve living conditions, particularly in the world's most vulnerable regions.
Data Source
UN Habitat
The Data and Analytics Section (DAS) is the specialized statistics unit within UN-Habitat. The data section is responsible for overall data oversight across all urban monitoring domains within UN-Habitat, methodological developments, supporting member states in their monitoring efforts around global agenda such as the SDGs, implementing direct data collection and compilation, providing data to UN-Habitat global reports, and publicly and openly disseminating urban data through its urban indicators programme.
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