4. the ‘africa land and infrastructure city scan
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Urban infrastructure in Sub-Saharan Africa
Harnessing land values, housing and transport
Presented by Brandon Finn, ACC Research by Brandon Finn, Sean Walsh and Ian Palmer
20 July 2015
The Africa Land and Infrastructure City Scan:
A profile of 31 cities in Sub-Saharan Africa
Purpose of the scan
Run a high level assessment of the potential of cities in Sub-Saharan Africa to apply Land-
Based Financing
Recognition of the complexity of factors
impacting in potential and the shortage of
good data on SSA cities
Multi-criteria analysis: Assessing the relative potential of cities to
apply land-based financing
Calculate overall score
Examine results
Identify criteria
Identify cities
Adjust indicators to scores
Assign weights
Identify indicators
Selection of cities
- 2,000 4,000 6,000 8,000 10,000 12,000 14,000
Lilongwe
Cotonou
Mombasa
Kigali
Maputo
Pretoria
Harare
Brazzaville
Kampala
Conakry
Lusaka
Bamako
Abuja
Ouagadougou
Yaounde
Douala
Durban
Addis Ababa
Ibadan
Dakar
Cape Town
Kano
Nairobi
Johannesburg
Kumasi
Dar es Salaam
Accra
Abidjan
Luanda
Kinshasa
Lagos
2015 population - '000s
Top 31 – all above 1 million people bar Lilongwe
UN Habitat - State of African Cities – escalated (plus some additional data)
Economic
growth;
Urbanisation
Finance
Bulk,
connector and
social
Infrastructure
Developers Financiers
Space (property) market
Supply Demand
Value capture
Land
Local government
Planning and
land use
management
State
Land-based financing relationships
Demand for property
Access to finance
Active developers
Effective City
Growing economy
Effective State
Access to land
Well developed economy
Ability to pay for property
Degree of secure tenure
Access to banking
Ease of registering ownership
Ease of getting land use approval
Ease of doing business
Growing population
Service provision track record
Sound governance
Citizens willing to pay for services
Functions devolved
Adequate technical capacity
Financially viable
Level of transfers to LG
Commitment to support LG
City GVA growth (OE)
City GVA/capita (OE)
% High income households (OE)
Sum of EFW, MCC and WEF ratings
WDI – banks per 100,000 capita
Ease of business: registration ranking
Team rating
Ease of business: other indicators
Rate of population growth (OE)
Composite elec & watsan access
WB governance indicator
% household income to services (OE)
UCLGA indicator on constitution
WEF professional management; skills
UCLGA indicator on own revenue
WDI indicator
UCLG rating
C
C
C
C
C
N
N
N
N
C
C
N
N
Effective planning and LUM Existence of master plan C
Primary Criterion Secondary Criterion Indicator (see key) City or National
Land use formally approved Team rating
N
N
N
N
C
Sup
ply
of
pro
per
ty
Primary criterion Primary Weight
Secondary criterion Secondary Weight
Demand for property 10 Well-developed economy 20
Growing economy 30
Growing population 20
Ability to pay for property 30
Access to land 30 Land use formally approved 30
Ease of getting land use approval 30
Degree of secure tenure 30 Ease of registering land 10
Active developers 10 Extent to which developers can function easily
100
Ease of access to property related finance
10 Access to banking 100
Effective city 30 Functions devolved 20
Service provision track record 30 Financially viable 10 Adequate technical capacity 20
Effective planning and LUM 10
Citizens willing to pay for services 10
Effective State 10 Extent to which governance is effective
40
Commitment to support LG 50
Extent to which transfers are made to LG
10
Weighting –base position
Inputs:
• Set up for any cities
• Add any data
• Calculate relationships between data (e.g. GDP/capita)
• Apply multi-criteria analysis
Present results:
• Map
• Table
• Graph
Download results
Developed by Sean Walsh of Webfresh – for ACC and DFID
Africa Land and Infrastructure City Scan
Interactive web-based database
Data for cities with link to Google maps
Set up Multi-Criteria Analysis and
easy adjusting of weights
Primary and secondary criteria facility
Criterion grouping Weight
shift Relative weights
Swing in MCA score
Demand for
property
Supply side
factors
Effective city
Effective State
Default 10 50 30 10
Effective city 30 to 50 7 36 50 7 0.22
Effective city 30 to 10 13 64 10 13 0.21
Demand for property 10 to 30 30 39 23 8 0.24
Effective state 10 to 30 7 39 24 30 0.12
Supply side factors 50 to 30 14 30 42 14 0.20
Test different weightings
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00C
on
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GD
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US
$ @
PP
P (
ba
rs)
MC
A s
core
(d
ots
)Headline results: MCA scores
Plot against any other criteria – GDP per capita
in this case
End
Urban infrastructure in Sub-Saharan Africa – harnessing land
values, housing and transport
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