Modelling of Demand for Services Tian Cla as Sens

8/2/2019 Modelling of Demand for Services Tian Cla as Sens

1/58

MODELLING OF DEMAND FOR

SERVICES THROUGH THE CHARACTERISATION OF TOWNS

AND AREAS THROUGH STANDARD DWELLING TYPES

Presented by:

Tian ClaassensJon Lijnes


2/58

CONTENTS

1. BACKGROUND

2. BIGEN AFRICA

3. RISK MANAGEMENT

4. COMMON MISTAKES

5. BIGEN AFRICA METHODOLOGY

6. CASE STUDY

7. USING DEMAND MODEL FOR RISK MITIGATION

8. CONCLUSIONS


3/58

BACKGROUND

In 1994 South Africa launched an initiative to extend basic services (water,

sanitation & electricity supply) to all communities Large parts of the country and communities remained un-serviced due to

the previous dispensation of apartheid

Government has also launched a housing initiative to:

Convert informal settlements to formal settlements

Convert shacks to formal dwellings

Provide each citizen with a decent house


4/58

BACKGROUND contd Also driven by the UN Millennium Development Goals

South Africa has committed inter alia to:

Target 9: integrate the principles of sustainable development into countrypolicies and programmes and reverse the loss of environmental resources

Target 10: halve by 2015 the proportion of people without sustainable access

to safe drinking water

Target 11: by 2020 to have achieved a significant improvement in the lives of at

least 100 million slum dwellers


5/58

BACKGROUND contdMAIN AIM IS TO TURN THIS

INTO THIS!


6/58

BACKGROUND Contd BACKLOG Quantified expenditure to meet the millennium

goals

15 years later and extent of backlog is growing: Limited government resources

Inefficient expenditure

Unsustainable expenditure/investments

Widely recognised that extent of backlog is too large to be

eradicated through government resources only

Commercial finance (project finance) will play a key role in next15 years

Risk identification, management & mitigation becomes key

factors for success


7/58

BIGEN AFRICA Development activist company

Multi-disciplinary project teams including:

Engineers (civil, electrical, township etc.)

Project managers

Town planners

Project finance specialists

Package major infrastructure and housing projects for implementation and

finance

Focus on risk management

Mitigate risks that impact on bankability of projects


8/58

RISK MANAGEMENT What are the key risks in this context?

Estimating demand for service in a target area

Growth in demand?

Cost of service versus affordability credit risk

Ability/strategy to recover costs

Sustainability

Broadly referred to as Demand Risk

What are the consequences of Demand Risk?

Inappropriate system design

Capital inefficiency (scarce resource!)

Low cost recovery high default rate

Unsustainable systems manifested through low maintenance expenditure

Failure of supply default

Total system failure

Non-bankable projects


9/58

COMMON MISTAKES REFLECTING DEMAND RISK

Existing supply as basis

Ignores current restrictions in systems

Ignores losses in systems (can be major e.g. > 40%)

Ignores incorrect data (readings) Population numbers as basis

Ignores inaccuracy of census info (RSA)

Ignores differences in consumption patterns of socio economic groups (housing

typologies)

Ignores changes in demand because of socio economic shifts

Inappropriate growth modelling

Uniform growth rate applied in perpetuity Measured short-term spurts projected over the long term


10/58

THE BIGEN AFRICA METHODOLOGY Demand risk is encountered on every infrastructure project

Key risk that inhibits availability of project finance

Bigen Africa has developed a methodology to:

Quantify demand risk (Partially) mitigate demand risk

Price demand risk (in a municipal context)

This methodology is based on the following premises:

Key drivers of demand in a region or area:

Total number of households in the region

Total commercial/institutional floor area in the region

Household demand is a function of the housing typology Key typology characteristic is the number of toilets

Growth in demand is primarily driven through growth in the

number of dwellings or commercial floor space


11/58

THE BIGEN AFRICA METHODOLOGY From these premises it follows that:

If we know the total number of houses in an area

And each house has been characterised in terms of housing typology

Then demand for a service in the area can be determined Similarly:

If we forecast how the number of houses of each typology in the area will

grow Then we can forecast growth of demand in the area

For characterisation of houses in terms of typology we use standard dwelling

types (SDTs)


12/58

EFFLUENT FLOW (%OF WATER DEMAND)Seasonalit y of demandWater Demand (kl/ month) Typically found in all towns & villages as well as most urbanareas Enjoys a full level of service:

Water supply (house or yard connection) Sanitation (water borne) Electricity supply

Serviced through dirt or paved roads Dwelling sizes typically range between 34 m 2 and 80 m 2

In metropolitan areas, erf sizes will typically be smaller than350m 2

Units feature a single toilet Housing 2 to 8 people 1 out of 3 units uses electricity for cooking 9 out of 10 units feature an electrical geyser

Example: Low Capacity SDT


13/58

LIST OF SDTS

Low level of service (LLOS)

Intermediate level of service (ILOS) Low capacity (LCAP)

Medium capacity

(M

CAP) High capacity (HCAP)

Medium capacity type 2 (MCAP2) High capacity type 2 (HCAP2) Commercial/institutional (COMMINST)


14/58

SDT Examples: LLOS & ILOS


15/58

SDT Examples: LCap

WITH BACK YARD DWELLINGS


16/58

SDT Examples: MCap


17/58

SDT Examples: HCap


18/58

SDT Examples: Comm/Inst

OFFICES

SCHOOLS

MUNICIPAL

SHOPS


19/58

ADVANTAGES OF USING SDTS Rapid model development Model simplicity Relative ease of obtaining an accurate modelling basis aerial

photography and counts Elimination of individually driven estimation errors Understanding of the model by a wider audience is enhanced as most

people can generally and readily identify with the SDTs and their associated demand for services

Integration is facilitated with a logical connection to tariff and other policies

Standardisation of models across projects, municipalities and regions Benchmarking of models across projects, municipalities and regions


20/58

GENERAL SETUP OF MODEL

Identify area to be modelled Identify/set up sub areas if required

Quantify total number of dwellings in area: Aerial photography Physical counts

Area based

extrapolation Other estimates

Classify dwellings in terms of SDTs Formulate growth model for each SDT Run @Risk simulation Extract 5 demand scenarios from simulation data


21/58

CASE STUDYSOL PLAATJE LOCAL MUNICIPALITY


22/58

LOCALITY

GROUND COVER INDICATES ARID NATURE OF AREA


23/58

CONTEXT

Large Variation in Land Use

KIMBERLEY BIG HOLECITY CENTREHADISON PARKGALESHEWE

RETSWELELEROODEPANKAMFERS DAM

FLAMINGO ISLAND

ECOLOGICAL AND ENVIRONMENTAL CRISIS


24/58

CONTEXTDrive to establish bulk infrastructure to alleviate housing backlogs

All developments constrained due to inadequacy of services

Overloading of existing reticulation infrastructure (back yard dwellings)

Shortfall of income due to inadequate tariff structures

Inability to raise loans due to shortfall of income Unaccounted for water and sewage spills

Environmental effects on Kamfers dam and flamingo breeding colony

Influenced by specific interest groups, resulting in wrong decisions


25/58

MODELLING PROCESS Municipal area was divided into demand zones

Based on Natural drainage areas for sewage

Electrical supply

zones

and

Water supply zones Road service areas

Reasonably uniform dwelling characteristics Spatial constraints

Physical count of dwellings carried out

In each demand zone Based on aerial photography (Dec 2006)


26/58

MODELLING PROCESS (Ctd)

Estimate of back yard dwellings in each demand zone determined Direct field observations (samples) for each zone

Statistical estimation known variance & error

Commercial/industrial /institutional structures/dwellings

No bulk data available from municipality

Direct field observations (sample) for each zone Statistical estimation known variance & error


27/58

KIMBERLEY: DEMAND ZONES


28/58

KIMBERLEY: RESIDENTIAL SDT DATA


29/58

COMM/INST SDT DATA


30/58

MODEL RESULTS: TOTAL HOUSING


31/58

MODEL RESULTS: TOTAL HOUSING

5.0% 90.0% 5.0%

59.93 66.15

5

6

5

8

6

0

6

2

6

4

6

6

6

8

7

0

7

2

7

4

Values in Thousands

0.0

0.5

1.0

1.5

2.0

2.5

V a

l u e s x

1 0 ^ - 4

30-Sep-14 / Total Dwellings

30-Sep-14 / Total Dwellings

Minimum 56455.1467Maximum 72604.6100Mean 62915.0590Std Dev 1898.1005

Values 1


32/58

MODEL RESULTS: TOTAL NEW HOUSING


33/58

MODEL RESULTS: INFORMAL HOUSING


34/58

MODEL RESULTS: TOTAL HOUSES PER ZONE


35/58

MODEL RESULTS: NEW HOUSES PER ZONE


36/58

MODEL RESULTS: NEW TYPES OF HOUSES


37/58

MODEL RESULTSCOMM UNITS PER ZONE


38/58

MODEL RESULTS: WATER DEMAND

ORIGINAL PLANNING WAS TO INCREASETHE EXISTING TREATMENT WORKS

CAPACITY BY 60Ml/D


39/58

MODEL RESULTS: WATER DEMAND (1)

5.0% 90.0% 5.0%

62.95 80.21

5 0

5 5

6 0

6 5

7 0

7 5

8 0

8 5

9 0

9 5

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

30-Sep-14 / Total water consumption (Ml/day)

30-Sep-14 / Total waterconsumption (Ml/day)

Minimum 54.7288Maximum 91.1906Mean 71.3600

Std Dev 5.3609 Values 1


40/58



41/58



42/58

RESULTS: ABNORMAL WATER LOSSES


43/58

MODEL RESULTS: SEWAGE

ORIGINAL PLANNING WAS TO REHABILITATETHE EXISTING HOMEVALE WORKS WITHOUT

PROVIDING ADDITIONAL CAPACITY


44/58

MODEL RESULTS: SEWAGE (2)


45/58


5.0% 90.0% 5.0%

46.74 61.41

4 0

4 5

5 0

5 5

6 0

6 5

7 0

7 50.00

0.01

0.02

0.03

0.04

0.05

0.060.07

0.08

0.09

0.10

30-Sep-14 / Total sewageeffluent (Ml/day)

Minimum 40.4875Maximum 72.7238Mean 53.7802Std Dev 4.5141

Values


46/58



47/58

MODEL RESULTS: ENERGY DEMAND


48/58



49/58



50/58

MODEL RESULTS: POWER DEMAND

ORIGINAL PLANNING WAS TO ADDEXTENSIVE BULK ELECTRICAL

INFRASTRUCTURE TO ABOVE 200 MVA


51/58

INTERPRETATION OF RESULTS Water supply:

No need to increase capacity of main supply system Focus on elimination of losses Demand management through appropriate tariffs & tariff

structure

Sewage treatment: Critical to expand capacity to 70 Ml/d vs 48 Ml/d as planned Critical to divert treated effluent away from Kamfers dam

New capacity to be provided on new site


52/58

INTERPRETATION OF RESULTS contd

Electricity supply: No need to increase capacity to 200 MVA Capital expenditure should be limited to:

Refurbishment Enhancing firm capacity

Enhancing reliability

of

system

USING DEMAND MODELFOR RISK


53/58

USING DEMAND MODEL FOR RISK

MITIGATION

In a project finance scenario 2 key risks must be mitigated: Demand risk Cost recovery risk

Demand model is a key tool to quantify these 2 risks: Linked to detailed Financial model Used to design suitable mitigation mechanisms


54/58

MITIGATING DEMAND RISK

Difference quantifies demand riskThis risk is mitigated as follows:1. Infrastructure is sized for HIGH SCENARIO 2. Income projections in Financial model

based on EXPECTED SCENARIO 3. Project tested for financial robustness at

LOW SCENARIO 4. Key parameter to adjust robustness:

TARIFF

MITIGATINGCOSTRECOVERYRISK


55/58

MITIGATING COST RECOVERY RISK Cost recovery risk (perceptions) vary for SDTs:

LLOS, ILOS & LCAP: High Risk MCAP, HCAP & COMM/INST: Low risk

Two key problems: Financiers typically confuse population size & demand Municipalities typically use uniform cost recovery strategies across the

board

Through the demand model we shift paradigms to: Prove the 80:20 rule Understand that risk determined by the Zone not the SDT Accept different cost recovery strategies for different zones

MITIGATINGCOSTRECOVERYRISK


56/58

MITIGATING COST RECOVERY RISK


57/58

CONCLUSIONS

Using the model we now understand: What drives demand for services (housing) Where the demand for services are

Where the demand will be in future Who will use the services

Model forms the basis of: Engineering/planning

Financial model Revenue model & strategy Affordability analysis

Integration between

services

housing,

water,

sanitation,

electricity

etc.

Model is the critical tool for risk analysis in all of these applications


58/58

THANK YOU FOR YOURATTENTION

Modelling of Demand for Services Tian Cla as Sens

Documents

Transcript of Modelling of Demand for Services Tian Cla as Sens