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

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2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

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1.00C

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MC

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)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