Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte

 
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Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Covid-19 scenario modelling tool
for local authorities
Version 2.0 | 15 September 2020
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Version Control

 Version 1.0 (8 September 2020)
 Draft version for user testing

 Version 2.0 (15 September 2020)
 Initial version provided to Councils with Workshops & Model v 2.0

© 2020. For information, contact Deloitte                            Scenario Modelling Tools   User Guidance v1.0 | September 2020   2
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Executive Summary

© 2020. For information, contact Deloitte.   Scenario Modelling Tools   User Guidance v1.0 | September 2020   3
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Covid-19 Scenario Modelling Tools
 DIA are supporting Local Government to plan post-Covid recovery

                   What is this project about?                                                                    To find out more

    DIA is working with Deloitte to develop a set of scenario                                           DIA and Deloitte are hosting a number of virtual
    modelling tools to assist local authorities with LTP                                                workshops to preview the Scenario Modelling Tools
    scenario modelling of the potential financial impact of                                             and invite you to nominate attendees from your
    different Covid economic scenarios and different policy                                             Council. This would be most interest to Council
    responses. The tools are designed to assist Councils to                                             Officers that are involved in:
    ask ‘what if’ questions such as:                                                                    • Developing Council strategic Covid response,
    •     What could be the financial impact on Council cash                                            • LTP/AP planning,
                                                                      Covid-19 Scenario Modelling
          flow, income, debt levels and debt headroom under                 Tools comprise:             • Finance/Treasury/LGFA funding,
          different post-Covid economic scenarios?
                                                                  •    Deloitte Access Economics        • Data insights and analytics.
    •     How might Council policy responses such as rate              Regional Economic Scenarios
                                                                                                        The project team may also present the Scenario
          deferrals, rating remissions, changes in variable            (High and Low cases)
                                                                  •    Interactive Data Visualization   Modelling Tools at local government forums.
          fees/charges, changes in income from CCOs, revised
                                                                       Dashboard                        Requested Action:
          capex or opex profiles or targeted grants affect debt   •    LTP Scenario Model (a
          headroom or changes in rates?                                separate Excel template)         Please forward this pack to the relevant Council
                                                                                                        Officers who would find this tool useful and contact
    Click on the following link to preview the regional                                                 Lauren Thompson to reserve a workshop place.
    Deloitte Economic Scenarios and data visualisation
    dashboard, which are key artefacts that comprise the
    Scenario Modelling Tools package.                                                                   Lauren Thompson
                                                                                                        LGModellingTool@dia.govt.nz
    Or copy the following link:                                                                         022 167 4770
    https://public.tableau.com/profile/deloitte.nz#!/vizhom
    e/Covid-19scenariomodellingtools/Cover

© 2020. For information, contact Deloitte                                                                     Scenario Modelling Tools   User Guidance v1.0 | September 2020   4
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Covid-19 Scenario Modelling Tools
 Core Functionality

                       Scenario Modelling Tools Functionality

      The scenario modelling tools are designed to work ‘out of the box’ and will come pre-populated with Councils’
      most recent LTP or AP submissions, with the functionality to:

      • Access national and regional data sets provided from MBIE, MSD, Statistics NZ and the Ministry of Health of
        key economic, health and economic activity indicators that are relevant to scenario planning via an
        interactive dashboard

      • Access to up to date (Sept 2020) National and up to 17 regional post-Covid economic impact and recovery
        scenarios developed by Deloitte Access Economics, covering: regional GDP, sector impact, population and
        unemployment metrics

      • Provide a mechanism to record the evidential base and change control/approval processes for “significant
        forecasting assumptions” that inform the LTP process

      • Scenario analysis functionality to analyse a range of economic shock scenarios and possible policy response
        scenarios

      • High level analysis of the potential impact on debt headroom against LGFA covenants

      • Sharing of best practice, lessons learnt, and approaches across councils with similar characteristics.

      • Allow optional customisation of the scenario modelling tools with more granular analysis of line items or
        more in depth regional economic analysis

      Access to the Scenario Modelling Excel Tools, populated with Council specific LTP data, and supporting user
      guidance will be made available to Councils after your attendance at one of the workshops.

© 2020. For information, contact Deloitte                                                                             Scenario Modelling Tools   User Guidance v1.0 | September 2020   5
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Overview of the Scenario Modelling Tools
 The Scenario Modelling tools comprise of three key components:

      (1) Deloitte Regional economic impact
                                                  (2) Interactive Data Visualization Dashboard                 (3) Scenario Modelling Tool
                     scenarios

 A set of High and Low economic impact            A web based interactive dashboard of national   A high level Excel based scenario modelling tool
 scenarios for 17 regions that provide forecast   and regional economic, health and economic      that is pre-populated with Councils’ 2018 LTP and
 regional scenarios of GDP, population change     activity indicators that can be used in         2021 AP data that supports a range of Covid
 and unemployment. Also provides an analysis      conjunction with Councils own local data sets   economic impact scenarios and policy response
 of current regional GDP by sector impact.        to inform economic shock scenarios and policy   scenarios. Designed to allow optional Council
                                                  response                                        specific customisation

© 2020. For information, contact Deloitte                                                             Scenario Modelling Tools   User Guidance v1.0 | September 2020   6
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Benefits of the Tools
 The Scenario Modelling tools are designed with the following objectives

                               Scenario Analysis
                                   • Pre-populated with Councils’ existing LTP/AP data to be used ‘out of the box’ with a basic level of scenario modelling functionality,
                                     based on practices developed by larger metro Councils which may be of particular interest to smaller councils
                                   • Ability to assess the size of the funding gap and indicative LGFA debt headroom under different economic shock scenarios or policy
                                     response scenarios

                                Access to relevant data in one place

                                 • Access to a range of national and regional economic, health and economic activity indicators including high frequency indicators via an
                                   interactive dashboard
                                 • Access to a set of High and Low economic forecast scenarios showing the potential ‘book end’ range of impacts of COVID-19 on regional GDP,
                                   employment, and demographics

                              A base tool and model to extend further optional analysis
                                   • The model and tools give councils a sound starting point to assess the impact of COVID-19 on the LTP
                                   • The model can be customised to specific councils needs with more granular line items (such as variable fees/charges, income from
                                     CCOs/investments or to test a range of specific local policy responses
                                   • Artefacts may be customized by individual Councils (eg. More in depth regional economic analysis by sector; analysis of locally relevant
                                     data sets; or integration of LTP scenario analysis with Councils’ own financial information systems)
                                   • Please contact the Deloitte Project Team for more information on customising the tool further

© 2020. For information, contact Deloitte                                                                                         Scenario Modelling Tools   User Guidance v1.0 | September 2020   7
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Where to get further support

                   Additional information                                                                                                                        Rotorua
                                                                                                          Auckland                                               Lee Gray
                                                                                                          Craig Robertson                                        leegray@deloitte.co.nz
       Virtual Workshops                                                                                  crrobertson@Deloitte.co.nz
                                                                        Key contacts:
       DIA and Deloitte are hosting a number of virtual workshops to
       preview the Scenario Modelling Tools and invite you to
                                                                        Core Project Team:
       nominate attendees from your Council.                            John Tan & Hilary Parker (Deloitte)
       Access to the Scenario Modelling Tools, populated with           Warren Ulusele & Lauren Thompson (DIA)
       Council specific LTP data and supporting user guidance will be   LGModellingTool@dia.govt.nz
       made available to Councils after these workshops.                                             Hamilton
                                                                        Economic forecasting:
                                                                                                     Brad Sherman
       Updated Data Visualisation Dashboard                             Liza van Der Merwe
                                                                        (04) 470 3545                brsherman@deloitte.co.nz
       Deloitte will continue to host and update the post-Covid data    elvandermerwe@deloitte.co.nz
       visualisation dashboard. Please contact us if you have
       suggestions for additional functionality or data sources for     Scenario Modelling:
       future iterations of the tool.                                   John Tan
       For further support                                              (04) 470 3676
                                                                        johntan@deloitte.co.nz
       Please contact either:                                                                                                                                    Wellington
                                                                        Data Visualisation Dashboard:                                                            John Tan
       • the core project team on the dedicated email:                  Adil Maqbool                                                                             johntan@deloitte.co.nz
         LGModellingTool@dia.govt.nz in the first instance, to RSVP     (09) 975 8553                                                       Christchurch
         for a workshop, request a copy of the Scenario Model or        admaqbool@deloitte.co.nz                                            David Seath
         any general enquiry, or                                                                                                            dseath@deloitte.co.nz

       • the Deloitte Access Economics team for further
         information on the economic forecasts, or                                                                                      Dunedin
                                                                                                                                        Mark Walker
       • your local Deloitte office contact
                                                                                                                                        mawalker@deloitte.co.nz
       who can assist with how to use the scenario modelling tools
       or to discuss options for further customisation of the tool or
       data sets.

© 2020. For information, contact Deloitte                                                                        Scenario Modelling Tools   User Guidance v1.0 | September 2020   8
Covid-19 scenario modelling tool for local authorities - Version 2.0 | 15 September 2020 - Deloitte
Interactive Data Visualization Dashboard

© 2020. For information, contact Deloitte.   Scenario Modelling Tools   User Guidance v1.0 | September 2020   9
Interactive Data Visualisation Dashboard
                  A web based dashboard of national and regional economic, health and economic activity indicators that can be used in conjunction with Councils’
                  own local data sets to inform economic shock scenarios and policy response

                  The tool displays the Regional and National Economic forecast scenarios developed by Deloitte Access Economics (current to September 2020).
                  These include a set of High and Low economic impact scenarios for 17 regions and provide a scenario forecast of GDP, population change and
                  unemployment. It also provides an analysis of current regional GDP and employment by sector.

       Click here to access the dashboard

       Or copy the following link: https://public.tableau.com/profile/deloitte.nz#!/vizhome/Covid-19scenariomodellingtools/Cover

       The following dashboards are available:

       •   Economic Scenarios | National
       •   Economic Scenarios | Regional
       •   Economic | Retail Activity
       •   Economic | Unemployment Support
       •   Economic | Employment
       •   Economic | Trade Activity
       •   Economic | Confidence
       •   Economic | Transport Activity
       •   Economic | Financing Activity
       •   Economic |Consumption Activity
       •   Economic | Manufacturing Activity
       •   Health | Covid status

       Select a tab to view the data. The
       following slides provide guidance on how
       to interpret selected dashboards.

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Interactive Data Visualisation Dashboard
    Regional economic forecasts

                                                                                      Graphs are dynamic. Hover
                                                                                        over to view details of
                                                                                         specific data points

The Regional Economic
Forecast scenarios are based
on 16 regions + Queenstown.
It may be possible to further
disaggregate specific TA’s or
groups of TA’s with additional
data or analysis

A description of the high and
                                                                                          Some graphs benchmark
low scenarios are in the
                                                                                           variables against NZ or
“Economic scenario” section
                                                                                            other TA’s or regions
of this Guidance

   © 2020. For information, contact Deloitte   Scenario Modelling Tools   User Guidance v1.0 | September 2020   11
Interactive Data Visualisation Dashboard
   Retail activity indicators

                                                                                               Most of the graphs compare 2020 with
Data is provided by                                                                             2019 to illustrate COVID-19 impacts.
regional council and by TA
where available.                                                                               E.g: national retail spending was down
                                                                                                    ~80% in April compared to the
In some cases, the data                                                                        previous year, but spending rebounded
provider’s definition of                                                                        strongly in July as we left lock-down.
‘region’ may not align with
Regional Council
boundaries

                                              The black lines are
                                              the national          Data from the dashboards can be
                                              benchmark             downloaded to inform the Councils’
                                              average               economic shock scenarios

  © 2020. For information, contact Deloitte                         Scenario Modelling Tools   User Guidance v1.0 | September 2020   12
Scenario Modelling Tool

© 2020. For information, contact Deloitte.   Scenario Modelling Tools   User Guidance v1.0 | September 2020   13
Scenario Modelling Tool
 Assessing potential COVID-19 impacts on long term plans and planning a policy response
   The model utilises two sets of scenarios.
   1. The first is the Economic Impact Scenarios, which assesses the potential impacts of COVID-19 on Council finances and the size of any potential funding gap.
   2. The second is a Policy Response Scenarios, which assesses how different policy levers may mitigate the funding gap or increase the funding gap through
       additional stimulus measures.

                                                                                                         Policy Response Scenario

                         1                                                                        2
                                            Economic impact Scenario
                                                                                                         • What are the potential policy responses and how
                                            • What are the potential impacts of
                                                                                                           will these affect the size of the funding gap?
                                              COVID on revenue items?
                                                                                                         • What might be the effect on Council debt
                                            • Could this result in a funding gap?
                                                                                                           headroom (measured against LGFA covenants)

                       Inputs: Regional Economic Scenarios                                            Outputs: A range of potential policy responses
                       & Data Visualisation tool                                                      Develop and manage a range of policy responses that address the
                       Scenario assumptions are supported by the Regional Economic                    potential impact of COVID-19.
                       Scenarios, data on the Interactive Data Visualisation Tool and other
                       data sources that Councils may hold. Use the forecasts and data to             Example 1: A new COVID-19 response package comprising targeted
                       develop an evidence base.                                                      grant funding of $1 million is offered to local food bank providers.
                                                                                                      This additional stimulus expenditure increases the funding gap.
                       Example: The regional economic scenarios of the Covid impact on GDP
                       estimate a reduction of between 8.5% to 18.3% in FY21 with a mid               Example 2: Councils may mitigate the size of the expected funding
                       point of 13.4%. Councils can use forecast change in GDP as one factor          gap by deferring capital expenditure.
                       to scale line items, noting that the economic impact on any line item is
                       expected to be influenced by multiple data points.                             The Scenario Model allows policy responses such as raising,
                                                                                                      deferring or remitting rates, adjusting fees/charges, flexing opex
                                                                                                      or capex, implementing new expenditure or raising debt.

© 2020. For information, contact Deloitte                                                                                Scenario Modelling Tools   User Guidance v1.0 | September 2020   14
Steps to populate and use the Scenario Modelling Tool
 Overview: Assess the impact of COVID-19 on the LTP and test policy responses

      Step 1: Populate the                          Step 2: Create Pre-Covid Baseline                  Step 3: Set the Economic                 Step 4: Set the Policy
        Model with Data                                                                                     Shock Scenario                       Response Scenario

                                                                                                                                                    Step 4: Define Policy
                                                                                                                                                    Response Scenarios
                                                                                                            Step 3: Define                      • The Policy Response
                                                                                                           Economic Shock                         Scenario may either
                                                                                                              Scenarios                           increase the size of the
                                                                           Step 2B: Include more
                                                                            granular information       • Create up to five                        funding gap (eg.
                                                                                                         scenarios                                targeted grants) or
                                              Step 2A: Create pre-                                                                                reduce the size of the
                                                                         • Optionally, choose to       • To add a scenario,
                                                COVID baseline                                                                                    funding gap (eg. defer
                                                                           add more detailed             populate the scenarios
            Step 1: Populate                • Select the 2018 LTP as       information (“High                                                     capex)
                                                                                                         with the expected
                Dataset                       the data source.             level” or “granular”) for     change (100% = 100%                    • Consider whether
                                                                           each LTP item.                of baseline)                             additional modelling of
     • FY2021 Annual Plan and               • Or input an alternative
       2018-2028 LTP is already in            baseline (such as draft    • You only need to use                                                   customised policy
                                                                                                       • Select “active scenario”
       the model.                             2021 LTP)                    the more granular                                                      responses such as
                                                                                                         is the scenario which
                                                                           option if you expect the                                               changes to pricing of
     • An draft 2021 LTP can also                                                                        you select to analyse.
                                                                           need to apply different                                                variable income lines,
       be input if available.                                                                                                                     service levels, rates
                                                                           economic shock factors
                                                                           or policy responses to                                                 profile, CCO dividends
                                                                           different line items.                                                  or debt structure may
                                                                                                                                                  be required

                                                                                                                              Iterate scenarios

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Step 1: Populate the database

                                                                                                  Step 1: Populate data

            Step 1: Populate                Step 1 is to populate Dataset. The 2018 LTP is the starting position, but can be updated with a draft 2021 LTP if available.
                Dataset                     Step 1A: Populate Model with 2018 LTP data in the 2018 LTP Inputs tab. This is already done by the model.
      • FY2021 Annual Plan and              Step 1B (Optional): Populate Model with 2021 AP Data in the 2021 AP Inputs tab. This is already done by the model.
        2018-2028 LTP is already in
        the model.                          Step 1C (Optional): Enter Normalisation Adjustments to the FIS-21AP sheet to generate forecasts for 2022 and 2023. This is
                                            only required if you intend to use the 2021 AP data in the calculations.
      • Draft 2021 LTP can also be
        input if available.                 Step 1D (Optional): The model has space to enter high level LTP information for the financial statements and funding impact
                                            statement. Enter your draft 2021 LTP data in the following tabs:
                                                    a. Balance Sheet (BS-21LTP)
                                                    b. Profit and Loss (LP-21LTP)
                                                    c. Financial management (FM-21LTP), and
                                                    d. Funding impact statement (all of council) (FIS-21LTP)
                                            The level of detail will be at a higher level than what Councils usually report on. There is opportunity to include additional line
                                            items in Step 2 if required.
                                            Step 1E: Check other inputs on the Control sheet for interest rates and debt balances are appropriate.

© 2020. For information, contact Deloitte                                                                                    Scenario Modelling Tools   User Guidance v1.0 | September 2020   16
Step 2: Create a pre-COVID19 baseline

                                                                                  Step 2: Create a Pre-COVID19 baseline

                                            Step 2A: In the Control tab:
          Step 2A: Create pre-
            COVID baseline                  • Select the preferred data source for each year. The starting point is the 2018 LTP but if you’ve entered 2021 LTP
                                              data, you can select this. The model will populate the table in this tab.
       • Select the baseline
         from available data

             Step 2B: Include
              more granular
               information
       • Select to add more
         detailed information               Step 2B: In the Economic Shock Input tab:
         (“High level” or
         “granular”) for each LTP           • Choose to run analysis using pre-populated 'High Level' data or optionally to use the more 'Granular' data using
         item.                                Council specific line items.
                                            • If the Granular option is selected, it must be populated for all time periods
                                            • Note that under the Granular option, the Model will self-balance against the LTP submission by putting any
                                              remaining balance into 'Other’

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Step 3: Set the Economic Shock Scenario

                                                                                 Step 3: Set the Economic Shock Scenario

                                             Note: Refer to the National and Regional Data Visualisation Dashboards for relevant data sets to inform the
                                             scenario profiles (see over page)
             Step 3: Define
            Economic Shock
                                            Step 3A: In the Dashboard tab:
               Scenarios
      • Option to create up to              • define the Economic Shock Scenario names
        five scenarios
      • To add a scenario,
        populate the scenarios
        with the expected
        change (100% = 100%
        of baseline)
      • Select “active scenario”
        is the scenario which
        you select to analyse.
                                            Step 3B: In the Economic Shock Input tab:
                                            • Scroll to Column T. There is spare columns to enter five Economic Shock Scenarios.
                                            • Set the Economic shock variables for each Economic Shock Scenario by changing scenario profiles for each Scenario (as a
                                              % of the Baseline). 100% = no change from Baseline. The example below shows Fees and Charges as 70% or 85% of
                                              baseline.

                                            • Scenarios allow line items to be scaled up or down, deferred or brought forward

© 2020. For information, contact Deloitte                                                                             Scenario Modelling Tools   User Guidance v1.0 | September 2020   18
Step 3: Set the Economic Shock Scenarios
 Making use of data provided in the data visualisation tool

   The Data Visualisation Tool includes information at a national, regional, and local council level on activity levels as well as forward looking forecasts of GDP,
   employment, and demographics. In conjunction with data sets held by Councils, the information in the tool can be used to inform and provide the evidence base to
   significant forecasting assumptions in the LTPs.
   Access the tool by copying the following link: https://public.tableau.com/profile/deloitte.nz#!/vizhome/Covid-19scenariomodellingtools/Cover
   Example: How will Porirua’s demand driven revenue items be affected in FY2021? Demand driven items could include fees and charges (such as swimming pools and
   libraries, and spots clubs), building consents, and revenue from grants and subsidies.

                         Change in the level of activity due to COVID                                            Forecasts of GDP and employment
                                  E.g. electronic card data.
                                                                                               Wellington Region is expected to have fared relatively well through COVID-19
      Consumer expenditure in Porirua City performed slightly better over the                     compared to the average New Zealand region. This is largely due to a
                                                                                               comparatively lower proportion of employment in sectors that were heavily
             lockdown periods compared to the national average.
                                                                                                                         impacted by COVID-19.

         Expenditure declined by 43%pa during L4 lockdown in Porirua city,                     The graph below shows regional employment by sector grouped by COVID-19
        compared to the same week a year earlier. This was in line with the                          impact. For example, 14% of the workforce is employed in Public
      national average. However, the city regained momentum quickly as the                                administration, which was one of the least hit sectors.
        country emerged from Level 3 and 4 lockdown, possibly reflecting a
      higher proportion of people choosing to work from home and buy local.

© 2020. For information, contact Deloitte                                                                              Scenario Modelling Tools   User Guidance v1.0 | September 2020   19
Step 4: Set the Policy Shock Scenario

                                                                                     Step 4: Set the Policy Shock Scenario
        Step 4: Define Policy               After setting the economic shock, change selected pre-defined policy levers.
        Response Scenarios
                                            Step 4A: In the Dashboard, define the Policy Response Scenario Names.                                     Levers can be
      Choose to model a selected                                                                                                                    flexed to model
      range of policy responses                                                                                                                     Policy responses
      such as:
      • changes to service levels,
      • changes in rates /
        deferrals / remissions,
      • changes to pricing of
        variable income lines,
      • Cash flows from CCO                 Step 4B: In Policy Response Input, set the Policy Response variables for each Policy response Scenario. There is space
        dividends                           for five scenarios. The Policy Response Scenario may either increase the size of the funding gap (eg. targeted grants) or
      The Model will default any            reduce the size of the funding gap (eg. defer capex)
      remaining change in
      cashflow as a result of the
      economic shock and policy
      response to the debt balance

        Policy examples:
  • “Can we afford to increase
     rates by 3% instead of 5%
            next year?”
                                            Step 4C: In Dashboard, Select the Active Policy Response Scenario
   • “Is there sufficient debt
       headroom to increase                 Assess the 'Size of the Funding Gap' and 'Impact on LGFA Debt' under the Active Policy Response Scenario
              capex?”                       Step 4D: Iterate Steps 3 and 4 to refine the LTP Scenarios. Record the Supporting Assumptions under each Scenario

© 2020. For information, contact Deloitte                                                                               Scenario Modelling Tools   User Guidance v1.0 | September 2020   20
Data sources
 A breakdown of the data used for the Scenario tool

  Data Source                               DescriptionDevelop
                                                        of dataUpdated set of Regional               Date
                                                          Economic Scenarios

  DIA – 2018/2028 LTP                       High level LTP3 scenarios, for up
                                                            data by Local     to 16
                                                                            and     regionsCouncil
                                                                                 Regional            2018-2028
                                                          with a 10 year outlook for
                                                          population, GDP and employment
  DIA – FY2020 Annual plan                  High level AP$35,000
                                                          data by+Local  and
                                                                   GST (in   Regional
                                                                           parallel with Council     2020
                                                          core model development

© 2020. For information, contact Deloitte                                                                   Scenario Modelling Tools   User Guidance v1.0 | September 2020   21
Model Assumptions
 List of modelling assumptions

 Users should note the following key assumptions.                                    • Fees & Charges scaling factor: The user inputs a positive or negative %
                                                                                       which is added or subtracted from total fees and charges.
 Baseline:
                                                                                     • Capex deferral: The user inputs a negative dollar amount of capex
 • The model uses a baseline forecast which by default is the 2018 LTP.                deferred and a positive dollar amount of capex incurred.
   Alternatively, users can select to use the 2021 AP or load a draft 2021 LTP
   to use as the baseline.                                                           • Opex deferral: The user inputs a negative dollar amount of opex
                                                                                       deferred and a positive dollar amount of opex incurred.
 • The model allows users to manually break down the baseline line items
   to a more granular view. Any balancing item between the manually                  • Opex on COVID related initiatives or policy responses: The user inputs
   entered line items and the total is captured within an ‘other’ category.            a positive dollar amount of additional opex.
 Economic shocks                                                                     • Sources of funding: The user inputs a positive dollar amount of
                                                                                       additional sources of funding.
 • A single shock factor can be applied to each line item in each year.
   Economic shock factors are inputs to the model. The model does not              • Note that all policy responses are applied independently. For example,
   calculate economic shock factors, however an example on how these                 both the rates scaling factor and rates remission percentage apply to
   shock factors can be developed is included in the model.                          pre-policy response total rates.
 Policy responses                                                                 Debt and Covenants
 • The model includes the following policy response tools:                        • The model calculates a funding balance after economic shock factors and
                                                                                    policy responses are applied. Any funding deficit or surplus accumulates
      • Rates scaling factor: The user inputs a positive or negative % which is     in a modelled debt facility as debt or cash. Interest is applied based on an
        added or subtracted from total rates.                                       input interest rate.
      • Rates deferral: The user inputs a negative dollar amount of rates         • Any existing debt facilities or cash in the baseline remain unchanged.
        deferred and a positive dollar amount of rates collected.                   Note that if different sources are used to construct a baseline, the
      • Rates remission: The user inputs a positive percentage which is             forecast debt balances from year to year may not reconcile. In this
        subtracted from total rates.                                                situation the LFGA covenant calculations may not be accurate.

© 2020. For information, contact Deloitte                                                                     Scenario Modelling Tools   User Guidance v1.0 | September 2020   22
Model Assumptions
 LGFA covenants

 The model calculates three LGFA covenants following a worked example                  Net interest as a percentage of total revenue.
 provided by LGFA. The model is based on the Funding Impact Statement                  •     Net interest is calculated as finance costs (2002) plus any net interest
 with a lower level of granularity than what would normally be used to                       calculated on the model debt facility resulting from the deficit or
 calculate covenants. Line items used to calculate LGFA covenants are                        surplus created as a result of economic shocks or policy responses.
 estimated on the following basis.
                                                                                       •     Total revenue is calculated in the same manner as net debt as a
 Net debt as a percentage of total revenue.                                                  percentage of total revenue.
 •      Net debt is calculated as                                                      Net interest as a percentage of annual rates income
        −        9006_Borrowing (total debt)                                           •     Net interest is calculated in the manner described above.
        −        Less 9004_Cash & financial investments/monetary assets                •     Annual rates income is calculated as the sum of:
        −        Plus / less any accumulated funding deficit or surplus created as a         −     1001_General rates, UAGC, rates penalties
                 result of economic shocks or policy responses is also included
                 within net debt.                                                            −     1002_Targeted rates (excluding metered water)
 •      Total revenue is calculated using the FIS, as the sum of:                            −     1002b_Targeted metered water rates
          •          Annual rates income:
                     −        1001_General rates, UAGC, rates penalties                    If Councils add more granularity when constructing their baseline,
                                                                                           adjustments are likely to be required to the LGFA covenant calculations.
                     −        1002_Targeted rates (excluding metered water)                Please refer to the LGFA for detailed guidance.
                     −        1002b_Targeted metered water rates
          •          Subsidies & grants income:
                     −        1003_Subsidies & grants for operating purposes
                     −        3001_Subsidies & grants for capital expenditure
          •          Other income:
                     −        1004_Fees & charges
                     −        1005b_Interest & dividends from investments
                     −        1006_Petrol tax, fines, infringement fees & other

© 2020. For information, contact Deloitte                                                                        Scenario Modelling Tools   User Guidance v1.0 | September 2020   23
Economic Scenarios

© 2020. For information, contact Deloitte.   Scenario Modelling Tools   User Guidance v1.0 | September 2020   24
COVID-19 | Economic scenarios
What the world could look like during and after the crisis passes

                                                                                  Key uncertainties
   Two economic scenarios
                                                                                  The next few slides outline a set of scenarios that
                                                                                  describe what the future could hold for New Zealand.
   • This section of the report looks at two economic scenarios.                  The scenarios are designed around three key
   • Making the right decision at the right time has never been more              uncertainties:
     important than now, and good decisions are based on good information.
     Scenarios are an appropriate and useful tool to plan as it allows Councils

                                                                                    1
     to test long term plans against a number of different outcomes.
   • Although the number of scenarios are endless during this time of                                 The effectiveness of the public health
     uncertainty, our resulting set of scenarios range from:                                            response and public compliance
            • The future we should prepare for (best-case)
            • The future we need to avoid (worst case).
   • Each scenario considers the likely outcome for the global economy, the
     domestic economy and the regional economy.

                                                                                    2
   • The scenarios are not predictions about what will happen, they are
     hypotheses about what could happen, and are designed to frame planning
                                                                                                        The effectiveness of Government
     discussions.
                                                                                                               economic support
   • Three key uncertainties were explored when developing scenarios,
     covering the health response and outcomes, effectiveness of fiscal policy,
     and outcomes in major trading partners.
   • The effectiveness of the health response is highly correlated with the
     speed of the economic recovery, where the effectiveness of fiscal

                                                                                    3
     stimulus also plays an important role. Meanwhile, the global environment
     is considered to be somewhat exogenous to the other uncertainties as
     this cannot be controlled by domestic policy. Despite being exogenous,                               Global economic conditions
     the global environment has a significant impact on the recovery of the
     regional economy.

                                                                                                                                               25
COVID-19 | Scenario 1
                                                                                  Scenario assumptions
Best Case                                                                         The public health response is effective. However, there is limited technology
                                                                                  improvement in case detection and tracing and this results in a growth in the number
A slow recovery                                                                   of cases over time. This COVID-19 scenario is therefore likely to start in July 2021
                                                                                  with a slow economic recovery.
   Mapping the scenario against three uncertainties                                          Health
                                                                                             • The success of the Alert Level 4 lockdown allows New Zealand to roll
                                                                                                back to Alert Level 1 for 3 months before a mild second outbreak
                Effectiveness of the health response
                                                                                                occurs in mid-late 2020. However, it is contained relatively quickly and
                                                                                                the country is restored to Alert Level 1 by late 2020 and into the
  Ineffective                                                 Effective                         foreseeable future.
                                                                                             • ​A vaccine is found by July 2021 and rolled out by late 2021.
                                                                                             • Borders begin to open in mid-2021 as the vaccine is rolled out.
                   Effectiveness of Government
                         economic support                                                    Global Economy
                                                                                             • The global outbreak is mostly contained in 2020, after which there is a
                                                                                                slow unwinding of travel bans over the following year.
  Ineffective                                               Effective
                                                                                             • China drives global recovery as it is the New Zealand’s largest export
                                                                                                partner (~2 times more exports than the country’s second largest
                                                                                                partner in Australia).
                      Global economic conditions                                             • Population growth slows given weaker global movement of people.
                                                                                             • Central banks maintain accommodative monetary policy settings
  Prolonged
  global                                                     Effective                          globally and support liquidity in financial markets
  recession                                                  recovery
                                                                                             Domestic economy
                                                                                             • The fiscal response focuses on providing income support and limiting
Why is this scenario plausible?                                                                business operating costs during the lockdown period.
                                                                                             • Fiscal measures provide some support to households, but
New Zealand is in the process to contain the second wave of COVID-19,
                                                                                               consumption is curtailed. Wage growth comes under pressure given
however globally there is some way to go until the outbreak is contained.
                                                                                               both increased unemployment and cost cutting measures taken during
Technological advances are limited. While the government is spending big, it is
                                                                                               the crisis.
still not enough to avoid low inflation and low investment leading to slow
                                                                                             • The New Zealand dollar comes under pressure and investors flock to
economic growth.
                                                                                               safe havens and the RBNZ works to keep interest rates low through a
Likelihood                                                                                     significant bond buying program.
This scenario is more likely to occur given Government’s response to date and                • Consumer and business sentiment remain weak post 2021, limiting
global recovery and global economic conditions.                                                investment and spending in the recovery.
                                                                                             • The health crisis and extended period of work from home 26
                                                                                               requirements results in a population flow out of major cities.
COVID-19 | Scenario 2
                                                                                     Scenario assumptions
Worst Case                                                                           In this scenario, New Zealand both struggles to contain COVID and the economic
                                                                                     recovery from the recession is delayed. This scenario sees waves of reinfection causing
Sustained economic disruption                                                        considerable loss of life and deep economic disruption over a prolonged period.
                                                                                                Health
    Mapping the scenario against the three uncertainties                                        • Further outbreak occurs in mid to late 2020 with cases lingering into
                                                                                                   2021. Forcing New Zealand to fluctuate between Alert Level 2 and
                                                                                                   Alert Level 3/4 until mid 2021 in order to contain the outbreaks.
                  Effectiveness of the health response                                          • There is a significant increase in demand for mental health services as
                                                                                                   prolonged closures result in increased cases. This extends into the
   Ineffective                                                   Effective                         recovery, reducing productivity and participation in the workforce.
                                                                                                • Vaccine is found in late 2021 and rolled out early 2022.
                                                                                                • Most international borders remain closed until a vaccine is available
                                                                                                   and distributed early 2022.
                    Effectiveness of Government
                          economic support                                                      Global Economy
                                                                                                • The global outbreak continues to cause difficulties for the economic
                                                               Effective
                                                                                                   recovery. China experiences a second COVID-19 outbreak which
   Ineffective
                                                                                                   causes a prolonged economic slowdown.
                                                                                                • Limited export demand from the US and China dampen the speed of
                                                                                                   the recovery. In addition, the inability to open borders hurts the large
                       Global economic conditions                                                  tourism sector.
    Prolonged                                                                                   • Central banks maintain accommodative monetary policy settings
    global                                                      Effective                          globally, with rates lower for longer in the US.
    recession                                                   recovery
                                                                                                Domestic economy
                                                                                                • New Zealand fiscal response is not strong enough resulting in
Why is this scenario plausible?                                                                   devastating loss to incomes and widespread job losses.
The COVID-19 pandemic becomes a prolonged crisis as a resurgence of the                         • Unemployment surges with some industries losing the majority of
virus creating panic and further uncertainty. We are in unprecedented times                       small businesses.
and small missteps now can have devastating consequences in the future. In                      • The public loses trust in the New Zealand economy which causes social
particular, people’s behaviour can become exceptionally individualistic when                      unrest and a sharp drop in spending and investment.
their own or their family’s wellbeing is threatened in the way described in this                • Monetary policy remains towards the zero lower bound with strong
scenario.                                                                                         quantitative easing.
                                                                                                • The New Zealand dollar devalues further due to our failed response
Likelihood                                                                                        relative to our global peers, and the country comes under pressure as
This scenario is less likely. New Zealand has social cohesion, an excellent health                our credit rating drops and public debt soars.
system, strong Government institutions and healthy Government debt to GDP                       • Limited travel, reduces population growth. This is exacerbated by27
ratio relative to other advanced economies.                                                       lower fertility rates and higher mortality rates.
Regional modelling of labour markets
   A high-level overview of how demographic variables and national forecasts drive labour market
   forecasts
   Key:

                                     Sourced data (scaled for consistency)
                                                                                              Demographic model (regional                                   National labour force
                                                                                                  and national level)                                            aggregates
                                     National aggregate models supplying
                                     baseline or scenario inputs

                                     Deloitte Regional employment models

                                                                                                                        Regional labour force
                                     User applied consistency analysis                                                       aggregates

• Modelling of regional data is top-down. Total employment by                                                                                             National employment
  industry is used to drive regional movements.                                                                                                           projections by industry

• The aggregate labour market (labour force, employment and                                     Consistency analysis
  unemployment) are set based on population by age, national
  participation and unemployment rates by age, and typical
  regional differences in participation and unemployment rates.
• Stats NZ data at the regional level is often inconsistent with
  national aggregates for the same industry (and occasionally
  data is not reported on a consistent basis for each). We scale
  our inputs by region and industry to conform with total                    Historic employment by
                                                                                                                       Regional employment by
  employment by region and total results by industry.                        region and industry from
                                                                                                                              industry
                                                                                     Stats NZ
• Consistency analysis is considered when movements in implied
  employment by industry, implied unemployment rates and
  underlying demographic trends come into conflict.

  © 2020. For information, contact Deloitte                                                                             Scenario Modelling Tools   User Guidance v1.0 | September 2020   28
Regional modelling of economic output (Regional output or regional GDP )
 A high-level overview of how regional output projections are derived

 Key:

                                     Sourced data (scaled for consistency)

                                     National aggregate models supplying baseline
                                     or scenario inputs and previous employment
                                     models

                                     Deloitte Regional economic models

                                                                       Historic regional economic
                                                                                                       Regional employment by
   • Historical Stats NZ data gives regional                          activity and employment by
                                                                                                              industry
     productivity differentials which capture                             region from Stats NZ
     differences in regional industry structure
     (everything from relative wealth effects
     on retail to different types of mining
     activity by region)                                              Implied regional productivity                                                          National value added
                                                                                                      Regional output by industry
                                                                              differentials                                                                 projections by industry
   • Employment levels and relative
     productivity create regional output by
     industry levels which are scaled to
     national projections.
                                                                                                        Regional output (GRP)
   • Regional output or GDP is a Production-
     based measure of output in this structure.

© 2020. For information, contact Deloitte                                                                           Scenario Modelling Tools   User Guidance v1.0 | September 2020   29
Explanation of outputs/nuances
 Clarification of key outputs/assumptions

                              Unemployment
                                                          Develop Updated set of Regional                                                        Develop Updated set of Regional
                              Generally, it would not be needed
                                                          Economicto Scenarios
                                                                     look beyond the unemployment rate but in unusual circumstances like during      pandemics
                                                                                                                                                 Economic          the definition of
                                                                                                                                                            Scenarios
                              the rate can come under scrutiny. To be considered officially unemployed, someone who is out of work must be both actively seeking and
                                                          3 scenarios, for up to 16 regions                                                      3 scenarios, for up to 16 regions
                              available for work. The downside
                                                          with a of
                                                                 10 this
                                                                    yeardefinition
                                                                         outlook foris that it excludes people out of work who get disheartened with  their job prospects and
                                                                                                                                                 with a 10 year outlook for
                              therefore stop actively seeking work. Although
                                                          population,            other measures such as utilisation and underemployment may show
                                                                       GDP and employment                                                              the impact
                                                                                                                                                 population,        of the
                                                                                                                                                              GDP and      pandemic
                                                                                                                                                                        employment
                              more widely, unemployment rate was used in the scenario forecasts due to data availability and the fact that it is a conventional measure of the
                                                          $35,000 + GST (in parallel with                                                        $35,000 + GST (in parallel with
                              labour market.              core model development                                                                 core model development

                              June to June years
                              The historic data and forecasts for the economic variables are on an annual basis and in the case of GDP, it is in annual percentage change. In this
                              case, June to June years were used i.e. GDP growth will be the difference from June 2020 to June 2021.

                              2019/2020 forecasts
                              Due to the timing and nature of Stats NZ economic data releases the 2020 June year will comprise of actual unemployment data but GDP and
                              population will be forecasted for that year. The year ending June 2019 represents the last full year unaffected by Covid and forms the basis of the
                              ‘pre Covid baseline’. Regional GDP Scenarios are also provided using a 2019 base year to align with the Scenario Modelling tool.

                              Population
                              As COVID-19 is likely a temporary shock, albeit being significant in nature, forecasts have assumed that regions will not experience population
                              exodus. This largely applies to the Queenstown-Lakes region as a large departure of the population which would have altered unemployment
                              forecasts due to a falling labour force. However, the economic scenarios have not assumed this, therefore resulting in relatively large modelled
                              increases in unemployment. Population forecasts are also forecasted using net migration, thus factoring in New Zealanders returning from
                              overseas which partially offsets the fall in immigration.

© 2020. For information, contact Deloitte                                                                                           Scenario Modelling Tools   User Guidance v1.0 | September 2020   30
Regional analysis
 Regions included in scenario analysis

2020 forecasted GDP impact                                 11.7%
(high scenario)

                                                                   8.6%
                          Less than national average
                                                                                           • The map shows high scenario GDP
                                                                                             forecasts and how they may impact
                          Greater than national average                                      across the various regions across New
                                                                      9.0%
                                                                              11.1%          Zealand.
                                                                                   10.4%
                                                                                           • The Queenstown-Lakes region has been
New Zealand is forecasted to                                                 10.4%
                                                             9.7%                            specifically split out from the Otago
experience a 9% drop in GDP under
the high scenario                                                     8.4%                   region as Queenstown has a sectoral
                                                                                             composition that is materially different
                                                                                             from the rest of the region. The reliance
                                                                                             on tourism drives this.
                                                                      8.5%
                                                                                           • Note that the Otago region also accounts
                                                    10.5%10.5%
                                                                                             for Queenstown in its forecasts.
                                            10.5%

                                            8.2%

                          16.4%

                                   8.7%
                10.8%

© 2020. For information, contact Deloitte                                                                Scenario Modelling Tools   User Guidance v1.0 | September 2020   31
Example region
 A spotlight on Bay of Plenty
 Economic scenarios have been developed to account for the potential impact of
 COVID-19 on regions across New Zealand. This slide presents the scenario outcomes          Bay of Plenty - real GDP, % change
 for the Bay of Plenty (BOP) region.                                                           15%
 Regional GDP                                   Develop Updated set of Regional                10%
 • The high scenario forecasts a rebound in GDPEconomic
                                                  for BOP Scenarios
                                                            in 2021 and 2022. The low
                                                                                                 5%
   scenario GDP contracts significantly over two3financial
                                                   scenarios, for upbefore
                                                            years,          rebounding in
                                                                     to 16 regions
   2021-22. Demand and supply side constraints  with a 10 year outlook for income and
                                                      in the  form   of  lost                    0%
   unemployment, and lockdown and border closures        respectively,
                                                population,             add to the slump
                                                             GDP and employment                 -5%
   in the economy. GDP never rebounds enough to recover the economic activity
                                                $35,000 + GST (in parallel with
   forfeited in 2020.                                                                         -10%
                                                           core model development
 Unemployment rate                                                                            -15%

 • In the high scenario unemployment is forecasted to remain elevated for some                -20%
   time as the impact of COVID-19 structurally damages industries. The low scenario
                                                                                              -25%
   expects unemployment to remain at historically high levels until 2023-2024.
 Population
 • The high scenario projects that BOP’s population growth returns to pre-virus                                                              Low Scenario              High Scenario
                                                                                            Source: Stats NZ, Deloitte analysis
   levels in 2024-2025 while the low scenario returns to pre-virus levels in 2025-26
   caused by a delayed vaccine release and borders opening.
 Bay of Plenty - unemployment rate (%)                                                      Bay of Plenty - change in population, persons
   13%                                                                                         7,000
   12%
                                                                                               6,000
   11%
   10%
                                                                                               5,000
    9%
    8%                                                                                         4,000
    7%
                                                                                               3,000
    6%
    5%
                                                                                               2,000
    4%
    3%                                                                                         1,000

                                            Low Scenario       High Scenario                                                                 Low Scenario              High Scenario
Source: Stats NZ, Deloitte analysis                                                         Source: Stats NZ, Deloitte analysis
© 2020. For information, contact Deloitte                                                                                         Scenario Modelling Tools   User Guidance v1.0 | September 2020   32
Core functionality/ potential incremental functionality
 Additional layers of data that are possible

                                                         Develop Updated set of Regional
                            Population                   Economic Scenarios
                                                         3 scenarios, for up to 16 regions
                               The demographic model has  thea functionality
                                                        with                 and
                                                               10 year outlook forcapability to break down population forecasts by region, age group and sex.
                                                        population, GDP
                               Example: males, aged 55-59 in Canterbury  and employment
                                                         $35,000 + GST (in parallel with
                               What is potentially required?
                                                         core model development
                               Updated/refined Stats NZ data.

                              Employment
                               The regional model have the ability to break down employment forecasts by region and ANZSIC06 industry
                               Example: employment in the agriculture, forestry and fishing industry in Northland

                               What is potentially required?
                               Updated data from Stats NZ in terms of employment by sector and region that adds more detail/granularity and input from councils
                               around their region’s economic profile.

                            Regional GDP
                               The GDP iteration of the regional model allows forecasts to be broken down by region and ANZISC06 industry
                               Example: GVA (gross value added i.e. contribution of a sector to GDP) for education and training in Wellington

                               What is potentially required?
                               Additional data from Stats NZ in terms of GVA by sector and region that adds more detail/granularity and input from councils around
                               their region’s economic profile.

© 2020. For information, contact Deloitte                                                                                       Scenario Modelling Tools   User Guidance v1.0 | September 2020   33
Data sources
 A breakdown of the data used for the economic scenario forecasts

   Data Source                              Description of data                                        Model                                                       Date
                                                        Develop Updated set of Regional
                                                        Economic Scenarios
   Stats NZ – 2018 Census                   Population by age, region and sex                          Demographic model                                           2001-2018
                                                        3 scenarios, for up to 16 regions
                                                        with a 10 year outlook for
                                                        population, GDP and employment
   Stats NZ – Infoshare                     Gross value added by ANZSIC06 national, quarterly          National forecasts                                          1987-2019
                                                        $35,000 + GST (in parallel with
                                                        core model development
   Stats NZ – Infoshare                     GDP by expenditure approach, quarterly                     National forecasts                                          1987-2019

   Stats NZ – Infoshare                     Labour force status national, annually                     National forecasts                                          1987-2019

   Stats NZ – customised request            Employment by ANZSIC06 industry national, quarterly        National & regional forecasts                               2003-2019

   Stats NZ – customised request            Employment by ANZSIC06 industry by region, annually        Regional forecasts                                          2009-2019

   Stats NZ – customised request            Labour force status by region, annually                    Regional forecasts                                          2001-2019

   Stats NZ – customised request            Regional GDP by ANZSIC06 industry, annually                Regional forecasts                                          2003-2018

   RBNZ - MPS                               Baseline scenario forecast for GDP and unemployment rate   Sense check for national scenario forecasts                   Dec 2019,
                                                                                                                                                                     Aug 2020

© 2020. For information, contact Deloitte                                                                      Scenario Modelling Tools   User Guidance v1.0 | September 2020    34
Appendices

© 2020. For information, contact Deloitte.   Scenario Modelling Tools   User Guidance v1.0 | September 2020   35
Frequently Asked Questions

 What is this project about?
 DIA have been coordinating the Local Government Covid-19 Recovery workstream, working in partnership with councils and other agencies to identify the challenges that
 the local government sector is facing post the Level 4 and Level 3 lockdowns. One of the top priorities of the Recovery Workstream is to support councils in preparing their
 2021-2031 Long-term Plans.
 DIA has commissioned the development of scenario modelling tools to assist local authorities in understanding the potential financial impact of different Covid economic
 scenarios and different policy responses. The focus of the analysis is on economic impact and policy response, rather than social impact, health response or other broader
 wellness measures.
 Who is involved in developing the tools?
 DIA commissioned Deloitte to assist with the development of the scenario modelling tools. Deloitte Access Economics provided the regional economic scenarios. A range of
 Government agencies, including Statistics NZ, MBIE, Ministry of Health and RBNZ contributed data shown on the Interactive Data Visualisation Dashboard.
 How much do the tools cost?
 DIA have covered the cost of developing version 1.0 of the tools to date and are continuing to provide funding during the 2021 fiscal year as part of the Local Government
 Recovery Workstream. Version 1.0 of the tools are to be made available to Councils free of charge.
 DIA funding support will enable local authorities to access the tools, assess potential improvements and to provide local authorities with limited user support from the
 project team to understand how best to utilise the tools. While individual local authorities may adapt the tools further for their own requirements and at their own cost, DIA
 may consider funding updates to the tool if there is a consensus from local authorities on functional requirements.
 Will central agencies be able to access the data?
 The data presented on the Interactive Data Visualisation Dashboard is publicly available. Each version of the LTP Scenario Model (the Excel workbook) will be pre-populated
 with an individual local authority’s 2018 LTP data and 2021 AP data and will be provided to the relevant local authority only. There is no mechanism in the LTP Scenario
 Model for any local authorities’ data to be provided to any other party or to any central agency. Although local authorities considering whether working regionally or in
 groups with similar characteristics would be useful to benchmark, calibrate and validate LTP scenario assumptions, especially given the economic interdependencies across
 adjacent local authority boundaries. While the project team can help with facilitating this, any sharing of an individual council’s data will be at the discretion of that council.
 Who do I contact for further assistance
 Contact details of the core project team are included on the ‘Notes’ tab of the Interactive Data Visualisation Dashboard and on the ‘where to get further support’ slide of the
 User Guidance. Specific enquiries relating to the Economic Forecasting, Scenario Modelling or Data Visualisation Dashboard can be addressed to the subject matter experts
 listed. The contacts listed in each regional Deloitte office may also be able to provide assistance with the tools in conjunction with your usual DIA regional director. Please
 contact the core project team on LGModellingTool@dia.govt.nz with your query in the first instance and one of the project team will respond.
 Who do I contact with feedback on the tools, data sets, or suggestions for improvements to the functionality?
 The project team welcomes your feedback on the tools, including any suggestions for additional data sets, or suggested improvements to the functionality. Please contact
 the core project team on LGModellingTool@dia.govt.nz in the first instance and one of the project team will respond.

© 2020. For information, contact Deloitte                                                                                   Scenario Modelling Tools   User Guidance v1.0 | September 2020   36
Terms of use of the scenario modelling tools

 The scenario modelling tools have been commissioned by DIA and developed in conjunction with Deloitte for use by local authorities
 based on information held by DIA, regional economic impact scenarios developed by Deloitte and a range of public sources. However,
 the scenario modelling tools have not been developed to meet the needs of any specific local authority and local authorities using the
 scenario modelling tools do so at their own risk.
 DIA permits local authorities to make use of the scenario modelling tools on the following terms:
 •      In no way does DIA or Deloitte guarantee or otherwise warrant that any financial forecasts scenarios of any entity will be achieved.
        Forecasts are inherently uncertain. They are predictions of future events which cannot be assured. They are based upon
        assumptions, many of which are beyond the control of the local authority and its management team. Actual results will vary from
        the forecasts and these variations may be significantly more or less favourable.
 •      Users of the scenario modelling tools do so at their own risk and acknowledge that neither DIA nor Deloitte have provided any
        specific advice to the user and neither DIA nor Deloitte accept any duty of care to any user who relies on any of the scenario
        modelling tools.
 •      Neither DIA nor Deloitte makes any representation of the accuracy of data contained on the dashboard that has been provided
        from other agencies or public sources.

© 2020. For information, contact Deloitte                                                         Scenario Modelling Tools   User Guidance v1.0 | September 2020   37
Restrictions on use of the Regional Economic Scenario Forecasts

 Deloitte Access Economics has developed the regional economic scenario forecasts contained in the dashboard as at September 2020.
 While Deloitte believe the scenario forecasts are a reasonable assessment of prospective trends in the relevant economies, due to the
 severity and duration of the pandemic being unknown, the forecasts are subject to forces others than economic factors, which is not
 normal. The scenario forecasts consider, where possible, the potential impact of Coronavirus (COVID-19) on the relevant economies. At
 the time of the publishing the forecasts, the situation is continuing to evolve, and many uncertainties remain as to the effect the
 COVID-19 crisis will have on the on the domestic and regional economies. Accordingly, the forecasts do not fully identify and quantify
 the impact of all COVID-19 related uncertainties and implications. Changes to market conditions could substantively affect the on the
 economies. These forecasts are best understood as a ‘most likely’ outcome around which unexpected (or unprojected) events will
 produce different outcomes.
 The Regional Economic Scenario Forecasts have been developed using a combination of publicly available data and proprietary Deloitte
 economic models. It may be possible to provide more detailed economic analysis of a specific sector or region, with access to
 additional local data and additional analysis. Please contact Liza van der Merwe from the Deloitte Access Economics team if you would
 like to discuss this further.

© 2020. For information, contact Deloitte                                                    Scenario Modelling Tools   User Guidance v1.0 | September 2020   38
Data Sources

 Other than the Regional Economic Scenario Forecasts, which have been developed by Deloitte Access Economics, all
 other data sources included on the dashboard have been provided from the following agencies: Statistics NZ, MBIE,
 MSD, Ministry of Health, and RBNZ. In some cases, the data sourced from public sector agencies may in turn be
 sourced from other sources (such as ANZ economic confidence indices). In some cases, the data is provided by the
 agency publicly and has been re-represented on this dashboard for convenience. In other cases, the agency has
 provided data in a format that is relevant for this project, eg with additional granularity at the local authority level. Any
 further updates to the dashboards will depend on the agencies continuing to provide the existing data in the current
 format.

 One of the key data sets used in this dashboard is sourced from Stats NZ's COVID-19 data portal, which gathers key
 high-frequency and near real-time economic indicators to help track the impact of COVID-19 on the economy. A
 subset of the data sets from this data portal are presented on this dashboard, focussing on key economic indicators
 and data sets that are available at a regional or local authority level of granularity. The full data portal can be accessed
 here: https://www.stats.govt.nz/experimental/covid-19-data-portal

© 2020. For information, contact Deloitte                                              Scenario Modelling Tools   User Guidance v1.0 | September 2020   39
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