THE GLOBAL URBAN OBSERVATORY DATABASES
Monitoring the Implementation of the Habitat Agenda
ANALYSIS OF URBAN INDICATORS
Report prepared by Joe Flood, Urban Resources (April 2001)
1.
INTRODUCTION
A first Global Urban Indicators Database (GUID1) was produced in 1996 in order to provide information on urban conditions and trends for the Habitat II Conference (Istanbul, 1996). This database collected information on 237 cities worldwide and was perhaps the first representative sample of urban indicators on a truly global basis.
As part of the statistical analysis of this database, a City Development Index (similar to the Human Development Index of UNDP) was derived which could be used to rank cities along their level of development, and as a baseline for comparative display of many indicators of urban conditions. Subsequent work with updated and modified versions of this index have revealed it to be a valuable tool for assessing the quality of life and the condition of city environments.
The Habitat Agenda and Resolutions 15/6 and 17/1 of the United Nations Commission on Human Settlements required the development of an indicators system representing the minimum data required to monitor changes in conditions in human settlements post Habitat II. UNCHS (Habitat) developed an indicators system that contains a set of 23 key indicators and nine lists of qualitative data. These are the minimum data required for reporting on shelter and urban development consistent with the twenty key areas of commitment in the universal reporting format.
The indicators are designed to measure performances and trends in the 20 selected key areas, and to measure progress in the implementation of the Habitat Agenda. Indicators provide a comprehensive picture of cities, which, with other indicators which may be chosen by countries, will provide a quantitative, comparative base for the condition of cities, and show progress towards achieving urban objectives.
The resultant Global Urban Indicators Database 2 (GUID2) prepared for Istanbul+5 includes data for 151 cities worldwide. It is intended to assess urban conditions and trends in the two years, 1993 and 1998, and to evaluate progress made between 1993 and 1998. It constitutes the main source of information for analyzing urban conditions, trends and progress for the Istanbul+5 meeting, June 2001.
Indicators were received from cities in the 6 regions used for analysis, according to Table 1, which also shows the total populations and urban populations of these regions.
Table 1. Sample size by region compared
with total national and urban populations.
|
Grouping |
Cities* |
Sample |
Countries |
Regional Population |
Urban population |
||
|
Africa |
29 |
17.7% |
45 |
568 |
9.9% |
186 |
7.0% |
|
Arab States |
14 |
8.5% |
17 |
256 |
4.5% |
144 |
5.4% |
|
Asia-Pacific |
28 |
21.3% |
34 |
1884 |
32.9% |
657 |
24.7% |
|
HIC |
9 |
3.7% |
17 |
784 |
13.7% |
607 |
22.8% |
|
LAC |
48 |
29.3% |
33 |
496 |
8.7% |
370 |
13.9% |
|
Transitional |
36 |
19.5% |
24 |
1747 |
30.5% |
696 |
26.2% |
|
TOTAL |
164 |
100% |
170 |
5735 |
100% |
2660 |
100% |
· This number includes 13 cities from the Asian Development Bank Cities Data Book, which had data collected on the same basis. These are included in the CDI analysis but not in other tables.
As for the 1993 collection, there is a substantial undersampling of the Highly Industrialised countries, where for many indicators there are not really enough cities to be statistically representative. There are also no cities from the largest countries, India and China. The LAC region has been oversampled, with a number of small cities surveyed, and also Africa.
2. SHELTER
HOUSING TENURE
Housing tenure tends to be institutional in nature and therefore will differ strongly between otherwise similar countries according to the regulatory framework, subsidies or controls applied to various sectors, the existence of mortgage finance, income distribution, urban growth, and land use planning controls. It therefore shows different patterns both between and within the broad regions of the world.
Table 3. Housing tenure
|
Region1 |
Owned |
Mortgaged |
Private rent |
Social |
Sub-tenant |
Squatter |
Homeless |
Other |
|
Africa |
40.8% |
3.4% |
31.3% |
5.2% |
4.5% |
9.3% |
1.0% |
4.5% |
|
Asia-Pacific |
61.4% |
3.6% |
23.4% |
0.9% |
0.3% |
2.1% |
1.5% |
6.8% |
|
LAC |
60.6% |
5.5% |
19.2% |
3.1% |
0.9% |
6.6% |
2.9% |
1.2% |
|
Transitional |
60.7% |
2.6% |
4.4% |
25.0% |
0.3% |
2.9% |
1.2% |
2.9% |
|
All developing |
57.1% |
4.0% |
17.2% |
10.3% |
1.4% |
5.1% |
1.6% |
4.3% |
Note
1. No tenure information was provided in
the Arab States (except Algiers) or in HIC.
Figure 2. Housing tenure
Privatisation
The major change in housing tenure is in the Transitional countries, where social housing has fallen from 44% to 25% of the total. Overall, the ownership level has risen from around 40% to almost 60%in these cities. As table 3, shows, this has been due to wholesale privatisation in some countries like Estonia, Armenia, Yugoslavia and Moldova, rapid privatisation in Croatia, Latvia and the Russian Federation, where ownership has doubled, and slower or more uneven changes in Poland, Mongolia, Laos and the Czech Republic.
Private rental and mortgages have also increased somewhat in the region (from 3.5% to 4.5%, and from 1.2% to 3.1% respectively), and homelessness has also increased (from 0.35% to 1.2%). Those countries which privatised early, such as Bulgaria and Slovenia, have seen a slight fall in ownership as new entrants to the housing market find it difficult to locate affordable housing. This trend can be expected to accelerate throughout the region.
Table 4. Privatisation of public housing,
transitional cities
|
|
|
1993 |
1998 |
||
|
City |
Country |
Ownership |
Social housing |
Ownership |
Social housing |
|
Sofia |
Bulgaria |
84.3% |
83.2% |
13.7% |
12.8% |
|
Ljubljana |
Slovenia |
80.0% |
14.0% |
77.0% |
15.0% |
|
Vilnius |
Lithuania |
70.0% |
19.0% |
84.4% |
3.6% |
|
Gdansk |
Poland |
52.9% |
0.0% |
63.0% |
37.0% |
|
Zagreb |
Croatia |
44.0% |
34.5% |
89.5% |
2.5% |
|
Belgrad |
Yugoslavia |
39.2% |
53.9% |
84.7% |
3.6% |
|
Omsk |
Russian Federation |
30.0% |
70.0% |
63.0% |
36.7% |
|
Tallinn |
Estonia |
25.0% |
75.0% |
91.8% |
0.3% |
|
Astrakhan |
Russian Federation |
25.0% |
75.0% |
51.5% |
48.5% |
|
Kostroma |
Russian Federation |
19.0% |
81.0% |
60.5% |
39.5% |
|
Nizhny Novgorod |
Russian Federation |
19.0% |
81.0% |
46.0% |
54.0% |
|
Chisinau |
Moldova |
17.9% |
54.2% |
75.0% |
20.0% |
|
Yerevan |
Armenia |
13.7% |
86.3% |
97.5% |
2.5% |
|
Riga |
Latvia |
13.4% |
77.3% |
24.7% |
52.9% |
|
Prague |
Czech Republic |
10.3% |
71.1% |
13.6% |
52.4% |
|
Veliky Novgorod |
Russian Federation |
7.0% |
93.0% |
37.4% |
62.6% |
|
Ulaanbaatar |
Mongolia |
0.% |
54.6% |
18.2% |
33.4% |
Mortgage finance
The development of mortgage finance has been patchy. It has fallen away in parts of Latin America such as Brazil, Chile, Columbia, Ecuador, Nicaragua, Paraguay and San Salvador, where it had been established to different degrees. It has also diminished in Thailand, where a strong system has weakened, and in a few African countries subject to unrest, such as the Congo. Some countries such as Bangladesh, Indonesia, the Czech Republic, Lithuania, Croatia and Cuba have been able to introduce housing finance or extend it to smaller cities, while others like Ghana, Uganda, Zimbabwe and Jordan have been able to expand their mortgage system.
Table 5. Proportion of dwellings with mortgages
|
City |
Country |
Region |
1993 Tenure.purchasing-all |
1998 Tenure.purchasing-all |
|
Zagreb |
Croatia |
Transitional |
1.0% |
17.0% |
|
Entebbe |
Uganda |
Africa |
4.0% |
14.0% |
|
Vilnius |
Lithuania |
Transitional |
0.0% |
6.0% |