MODELLING   OF   DEMAND   FOR
SERVICES   THROUGH   THE
CHARACTERISATION   OF   TOWNS
AND   AREAS   THROUGH
STANDARD   DWELLING   TYPES
Presented   by:
Tian   Claassens
Jon   Lijnes

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

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

BACKGROUND   cont’d
• Also driven by the UN Millennium Development Goals
• South Africa has committed inter alia to:
– Target 9: integrate the principles of sustainable development into country policies 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

MAIN AIM IS TO TURN THIS
INTO THIS!

BACKGROUND   Cont’d
• “ 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 next
15 years
• Risk identification, management & mitigation becomes key factors for success

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

• What are the key risks in this context?
– Estimating demand for service in a target area
– Growth in demand?
– Cost of service versus affordability
– Ability/strategy to recover costs
– Sustainability
• Broadly referred to as “Demand Risk” credit 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

•
•
COMMON   MISTAKES   REFLECTING
• Existing supply as basis
DEMAND   RISK
– 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

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

• 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 (“SDT’s”)

Example:   Low   Capacity   SDT
•
• 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 than
350m 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

LIST   OF   SDT’S
• Low   level   of   service   (“LLOS”)
• Intermediate   level   of   service   (“ILOS”)
• Low   capacity   (“L ‐ CAP”)
• Medium   capacity   (“M ‐ CAP”)
• High   capacity   (“H ‐ CAP”)
• Medium   capacity   type   2   (“M ‐ CAP2”)
• High   capacity   type   2   (“H ‐ CAP2”)
• Commercial/institutional   (“COMM ‐ INST)

SDT   Examples:   LLOS   &   ILOS

‐
WITH BACK YARD DWELLINGS

SDT   Examples:   M ‐ Cap

SDT   Examples:   H ‐ Cap

SDT   Examples:   Comm/Inst
SHOPS
OFFICES
SCHOOLS
MUNICIPAL

ADVANTAGES   OF   USING   SDT’S
• 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   SDT’s   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

GENERAL   SET ‐ UP   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   SDT’s
• Formulate   growth   model   for   each   SDT
• Run   @Risk   simulation
• Extract   5   demand   scenarios   from   simulation   data

SOL PLAATJE LOCAL MUNICIPALITY

LOCALITY
GROUND COVER INDICATES ARID NATURE OF AREA

FLAMINGO ISLAND
CITY CENTRE
KAMFERS DAM

CONTEXT
Drive   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

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)

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

KIMBERLEY:   DEMAND   ZONES

KIMBERLEY:   RESIDENTIAL   SDT   DATA

COMM/INST SDT   DATA

MODEL   RESULTS:   TOTAL   HOUSING

MODEL   RESULTS:   TOTAL   HOUSING
2.5
2.0
1.5
1.0
0.5
0.0
5.0%
30-Sep-14 / Total Dwellings
59.93
66.15
90.0% 5.0%
Values in Thousands
30-Sep-14 / Total Dwellings
Minimum
Maximum
Mean
Std Dev
Values
56455.1467
72604.6100
62915.0590
1898.1005
1000

MODEL   RESULTS:   TOTAL   NEW   HOUSING

MODEL   RESULTS:   TOTAL   HOUSES   PER   ZONE

MODEL   RESULTS:   NEW   HOUSES   PER   ZONE

MODEL   RESULTS:   NEW   TYPES   OF   HOUSES

MODEL   RESULTS ‐ COMM   UNITS   PER   ZONE

MODEL   RESULTS:   WATER   DEMAND
ORIGINAL PLANNING WAS TO INCREASE
THE EXISTING TREATMENT WORKS
CAPACITY BY 60Ml/D

MODEL   RESULTS:   WATER   DEMAND   (1)
0.09
30-Sep-14 / Total water consumption (Ml/day)
62.95
80.21
5.0% 90.0% 5.0%
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
30-Sep-14 / Total water consumption (Ml/day)
Minimum
Maximum
Mean
Std Dev
Values
54.7288
91.1906
71.3600
5.3609
1000

MODEL   RESULTS:   WATER   DEMAND   (2)

MODEL   RESULTS:   WATER   DEMAND   (3)

RESULTS:   ABNORMAL   WATER   LOSSES

MODEL   RESULTS:   SEWAGE
ORIGINAL PLANNING WAS TO REHABILITATE
THE EXISTING HOMEVALE WORKS WITHOUT
PROVIDING ADDITIONAL CAPACITY

MODEL   RESULTS:   SEWAGE   (2)

MODEL   RESULTS:   SEWAGE   (3)
0.06
0.05
0.04
0.03
0.02
0.01
0.00
0.10
0.09
0.08
0.07
5.0%
46.74
90.0%
61.41
5.0%
30-Sep-14 / Total sewage effluent (Ml/day)
Minimum
Maximum
Mean
Std Dev
Values
40.4875
72.7238
53.7802
4.5141
1000

MODEL   RESULTS:   SEWAGE   (4)

MODEL   RESULTS:   ENERGY   DEMAND

MODEL   RESULTS:   ENERGY   DEMAND

MODEL   RESULTS:   ENERGY   DEMAND

MODEL   RESULTS:   POWER   DEMAND
ORIGINAL PLANNING WAS TO ADD
EXTENSIVE BULK ELECTRICAL
INFRASTRUCTURE TO ABOVE 200 MVA

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

INTERPRETATION   OF   RESULTS   cont’d
– No   need   to   increase   capacity   to   200   MVA
– Capital   expenditure   should   be   limited   to:
• Refurbishment
• Enhancing   firm   capacity
• Enhancing   reliability   of   system

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

MITIGATING   DEMAND   RISK
Difference quantifies demand risk
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

MITIGATING   COST   RECOVERY   RISK
• Cost   recovery   risk   (perceptions)   vary   for   SDT’s:
– LLOS,   ILOS   &   L ‐ CAP:
– M ‐ CAP,   H ‐ CAP   &   COMM/INST:
High   Risk
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

MITIGATING   COST   RECOVERY   RISK

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