NTEM: Planning Data Version 7
Source: data.gov.uk (2024-07-04)

National Trip End Model (NTEM). The [freely available TEMPro software is required to view these datasets](https://www.gov.uk/government/publications/tempro-downloads).

The National Trip End Model (NTEM) model forecasts the growth in trip origin-destinations (or productions-attractions) up to 2051 for use in transport modelling. The forecasts take into account national projections of:

- population
- employment
- housing
- car ownership
- trip rates

NTEM forecasts are subject to uncertainty, especially when disaggregated to local zones or travel modes. Before using the NTEM forecasts, the following DfT guidance should be reviewed: 

- [TAG Unit M-4 Forecasting and Uncertainty](https://www.gov.uk/government/publications/webtag-tag-unit-m4-forecasting-and-uncertainty-november-2014) 
- [NTEM Planning Guidance](https://assets.dft.gov.uk/tempro/version7.2/NTEM72-documentation.zip) that provides an overview of the forecasts and the input data used (available in the NTEM Documentation Download below). 

Further technical details are available in the [NTEM specification](https://www.gov.uk/government/publications/webtag-si-ntem-sub-models-july-2016).

Queries regarding the NTEM forecasts should be sent to [Department for Transport](mailto:tempro@dft.gov.uk). To register to receive updates, email [TEMProMailingList@atkinsglobal.com](TEMProMailingList@atkinsglobal.com) to be added to the TEMPro distribution list. 

[GIS boundary files for the NTEM zone system are available](https://data.gov.uk/dataset/visual-boundaries-for-the-national-trip-end-model-ntem).

NTEM Planning Data 
Version 7 
 
Guidance Note 
Department for Transport 
July 2016 
  
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
 
Notice 
This document and its contents have been prepared and are intended solely for Department for Transport’s 
information and use in relation to Task SO7417: National Trip End Model Dataset Update. 
Atkins Ltd assumes no responsibility to any other party in respect of or arising out of or in connection with 
this document and/or its contents. 
This document has 104 pages including the cover. 
Document history 
Job number: 5138121 
Document ref: SO7417/2 
Revision 
Purpose description 
Originated 
Checked 
Reviewed 
Authorised 
Date 
Rev 1 
Draft issued for comment 
AM 
DW 
CLFL 
DW 
May 2016 
Rev 2 
Updated draft 
AM 
DW 
CLFL 
CLFL 
July 2016 
Rev 3 
Final version 
AM 
DW 
CLFL 
CLFL 
July 2016 
 
  
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
 
Table of contents  
 
 
 
 
 
 
 
1. 
Introduction 
6 
1.1. 
Overview 
6 
1.2. 
Outline of NTEM forecasting process 
6 
1.3. 
Summary of changes for NTEM 7 
8 
2. 
Planning data methodology 
11 
2.1. 
Summary 
11 
2.2. 
Objective 
11 
2.3. 
Basic inputs and spatial definitions 
13 
2.4. 
Households by control area 
15 
2.5. 
Population by control area 
17 
2.6. 
Population and households by zone 
18 
2.7. 
Employment and workers by zone 
19 
2.8. 
Balance workforce and workers 
19 
2.9. 
Update employment forecasts for change in worker profile 
24 
2.10. 
Summary of outputs from the Scenario Generator 
24 
3. 
2011 base year data 
26 
3.1. 
Summary 
26 
3.2. 
Population 
26 
3.3. 
Households 
28 
3.4. 
Dwellings 
29 
3.5. 
Employment 
30 
4. 
Forecast data and projections 
33 
4.1. 
Summary 
33 
4.2. 
Population 
33 
4.3. 
Households 
34 
4.4. 
Dwellings 
36 
4.5. 
Employment 
36 
5. 
Expected growth factors 
39 
5.1. 
Summary 
39 
5.2. 
Household EGFs 
40 
5.3. 
Employment EGFs 
44 
6. 
Planning data results 
45 
6.1. 
Introduction 
45 
6.2. 
Planning data (Scenario Generator) 
45 
6.3. 
National Car Ownership model (NATCOP) 
54 
6.4. 
National Trip End Model (CTripEnd) 
59 
Appendices 
67 
Appendix A. 
Geography 
68 
Appendix B. 
Segmentation definitions 
72 
Appendix C. 
Demographic parameters 
74 
Appendix D. 
Mathematic specification of methodology 
80 
Appendix E. 
Working Futures Industry Types 
98 
Appendix F. 
Source of dwelling trajectories 
99 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
 
Tables 
 
Table 1-1: NTEM zone system comparison 
9 
Table 1-2: NTEM age definitions 
10 
Table 2-1: Control total checks in Scenario Generator processing 
14 
Table 3-1: Base year population data sources 
27 
Table 3-2: Population by age, working status and gender (Great Britain, 2011) 
28 
Table 3-3: Base year household data sources 
28 
Table 3-4: Study area households totals (Great Britain, 2011) 
29 
Table 3-5: Base year dwelling data sources 
29 
Table 3-6: Study area dwellings totals (Great Britain, 2011) 
30 
Table 3-7: NTEM employment types 
30 
Table 3-8: Base year employment data sources 
31 
Table 3-9: Employment (jobs) by gender and working status (thousands, Great Britain, 2011) 
32 
Table 4-1: Forecast population input data 
33 
Table 4-2: Population projections by study area (thousands) 
34 
Table 4-3: Forecast year household data sources 
34 
Table 4-4: Single person household projections by study area (thousands) 
35 
Table 4-5: 2+ person household projections by study area (thousands) 
35 
Table 4-6: Forecast year dwellings by study area (thousands) 
36 
Table 4-7: NTEM 7 employment types and WF industry type for trend calculation 
38 
Table 4-8: Total employment (jobs) projections by type (thousands) 
38 
Table 5-1: NTEM 7 EGF area type definitions 
40 
Table 5-2: Household growth factors 
42 
Table 5-3: EGF source by NTEM employment type 
44 
Table 6-1: Comparison of GB forecast population data (thousands) 
46 
Table 6-2: Comparison of GB forecast employment (jobs) data (thousands) 
47 
Table 6-3: Comparison of GB forecast employment (workers) data (thousands) 
47 
Table 6-4: Proportion of total jobs by study area 
47 
Table 6-5: Comparison of GB forecast year household data (thousands) 
49 
Table 6-6: Comparison of GB forecast year dwellings data (thousands) 
50 
Table 6-7: Forecast car ownership comparison 
55 
Table 6-8: Total trip productions in GB comparison (millions per average day) 
59 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
 
Figures 
 
Figure 1-1: Basic steps in generating the NTEM 7 dataset 
7 
Figure 1-2: Structure of possible scenario generation 
8 
Figure 2-1: Summary of Scenario Generator process 
12 
Figure 2-2: Household estimation based on control area population changes 
15 
Figure 2-3: Allocation of households to control area 
17 
Figure 2-4: Derivation of households and population by zone 
18 
Figure 2-5: Estimation of zonal population 
20 
Figure 2-6: Derivation of the maximum number of workers in each NTEM zone by person type 
21 
Figure 2-7: Allocation of workers by age and gender in a balancing area 
22 
Figure 2-8: Allocating workers from balancing area to zones 
24 
Figure 4-1: National employment projections (OBR trend) 
37 
Figure 5-1: Expected growth factors applied as weights and trends 
39 
Figure 5-2: 2011 planning area types used in household EGF calculation 
41 
Figure 6-1: Comparison of planning data totals 
45 
Figure 6-2: Population growth (2011-2031) in NTEM 6.2 (left) and NTEM 7 (right) 
46 
Figure 6-3: Employment growth (workers, 2011-2031) in NTEM 6.2 (L) and NTEM 7 (R) 
48 
Figure 6-4: Households growth (2011-2031) in NTEM 6.2 (L) and NTEM 7 (R) 
49 
Figure 6-5: Comparison of GB forecast dwellings data 
50 
Figure 6-6: Average household size by study area 
51 
Figure 6-7: Average household size by control area – forecast year 2031 
51 
Figure 6-8: Vacancy rates by control area – forecast year 2031 
52 
Figure 6-9: Household capacity before reallocating or suppressing excess households 
53 
Figure 6-10: Households suppressed by study area (excluding Wider South East) 
53 
Figure 6-11: Households suppressed by study area (Wider South East) 
54 
Figure 6-12: Car ownership per household (2031) in NTEM 6.2 (L) and NTEM 7 (R) 
56 
Figure 6-13: Growth in households by car ownership (2011-2031) in NTEM 6.2 
57 
Figure 6-14: Growth in households by car ownership (2011-2031) in NTEM 7 
58 
Figure 6-15: Change in trip productions over time - NTEM 6.2 and NTEM 7 
60 
Figure 6-16: Growth in trip productions from 2011 – NTEM 6.2 and NTEM 7 
60 
Figure 6-17: Total trip productions by purpose in Great Britain – NTEM 7 
61 
Figure 6-18:  Proportion of total trip productions by purpose (2011) - NTEM 6.2 and NTEM 7 
62 
Figure 6-19:  Proportion of total trip productions by purpose (2031) - NTEM 6.2 and NTEM 7 
62 
Figure 6-20:  Average daily trip productions per household by study area - NTEM 6.2 
63 
Figure 6-21:  Average daily trip productions per household by study area - NTEM 7 
63 
Figure 6-22: Average daily trip productions per person by study area - NTEM 7 
64 
Figure 6-23: Growth in trip productions and attractions (2011-2031) in NTEM 6.2 (L) and NTEM 7 (R) 
65 
Figure 6-24: Growth rate of modal trip productions from 2011 – NTEM 6.2 and NTEM 7 
66 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
6
 
1. 
Introduction 
1.1. 
Overview 
1.1.1. This introduction provides an overview of the processes, methodology and data sources used to 
produce the NTEM 7 planning data set. This report is based on an earlier version produced for the preceding 
version of NTEM1. 
1.1.2. Section 2 describes the methodology used to convert the input assumptions into planning data 
forecasts. This methodology is implemented in a bespoke software tool, the Scenario Generator, which has 
been updated for version 7. This section explains the forecasting process, attempting to explain the more 
complex issues diagrammatically. The mathematical specification of this process is provided in Appendix D. 
1.1.3. Section 3 describes the data sources used in the derivation of 2011 base year inputs. These include 
base year data for population, households, dwellings and employment, segmented into the dimensions 
required by the Scenario Generator. 
1.1.4. Section 4 describes the forecast data requirements needed by the Scenario Generator. This covers 
use of trend-based population, household and employment figures. The production of a policy-based data set 
for dwellings is also described in this section. 
1.1.5. Section 5 describes the derivation of Expected Growth Factors (EGFs) used within the forecasting 
process. This includes a brief methodological overview and worked examples of the two methodologies. 
1.1.6. Section 6 summarises the results from the forecasting process, including car ownership and trip end 
projections. 
1.2. Outline of NTEM forecasting process 
1.2.1. The complete process involved in the production of NTEM forecasts can be seen in Figure 1-1. The 
process of creating forecast year planning data (the core of this note) involves running through the Scenario 
Generator process. This creates planning data forecasts for input into the National Car Ownership model and 
the National Trip End model. 
1.2.2. The Scenario Generator software reads in projections for population, households, dwellings and 
employment at an aggregate spatial level. In the case of policy based dwelling inputs these are extracted from 
published local authority trajectories. The software also reads in a set of Expected Growth Factors (EGFs), 
representing the levels of growth that would be expected in each zone, as a function of the area type of that 
zone. 
1.2.3. The software uses the base year demographic characteristics of each zone from the 2011 Census of 
Population. From this, the county/district level projections and the growth factors are used to generate 
consistent future year planning data at the zonal level. The process runs incrementally through time, with 
coarse-level changes over the next period being applied to the detailed zone-level results of the previous 
scenario.  
1.2.4. This process has been implemented in five year steps for each study area (roughly equivalent to the 
former Government Office Regions, Scotland and Wales), to a forecast year of 2051. Intermediate years 
released in the NTEM dataset via TEMPRO are generated through linear interpolation between the five year 
Scenario Generator forecasts. 
 
 
                                                     
1 NTEM Planning Data 6.2 – Guidance Note (WSP, 2011)  

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
7
 
Figure 1-1: Basic steps in generating the NTEM 7 dataset 
 
 
1.2.5. Figure 1-2 shows the general structure of a series of forecast runs for a particular study area. Each 
scenario builds on the last. At each point results may be exported to the National Trip End Model and the 
National Car Ownership Model. It is also possible to branch at a certain point and test different planning data 
inputs beginning from the results of a forecast scenario. 
1.2.6. Results from the NTEM forecasting suite are ultimately made available through the Department for 
Transport’s TEMPRO software. For information on this, please see the relevant documentation2. 
 
 
                                                     
2 https://www.gov.uk/government/collections/tempro [correct as of July 2016] 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
8
 
 
Figure 1-2: Structure of possible scenario generation 
 
 
1.3. Summary of changes for NTEM 7 
1.3.1. The following elements have been enhanced for NTEM 7: 
 
A new zoning system has been implemented, consisting of 7700 zones based primarily on middle-
layer super output areas; 
 
Age segmentation has been updated, accounting for general increases in working age and 
providing a greater level of disaggregation; 
 
Data sources have been updated to a 2011 base year, including model parameters; 
 
Forecast data sources have been updated to enable the ultimate time horizon of the model to be 
extended to 2051; 
 
Expected growth factors have been re-estimated based on updated planning area types; 
 
Balancing areas have been re-estimated based on 2011 TTWAs; 
 
A new trend based employment forecasting methodology has been adopted, based primarily on 
published sources of information;  
 
The National Car Ownership model has been re-estimated and updated to a 2011 base year, and 
to include additional area types and a cohort licence holding model3; 
 
A Trip Rate Forecasting Tool has been developed, enabling the production of time-varying trip 
rates4; and, 
 
Parameters for trip-end modelling have been re-estimated based upon an updated NTS sample; 
and, 
 
All software tools, databases and documentation have been updated. 
1.3.2. The same basic methodology and processing structure has been retained for the estimation of 
demographic planning forecasts, car ownership and trip ends. This includes all other aspects of segmentation, 
including definitions for employment types and land use indicators. 
                                                     
3 Detailed in the report “Estimation of the National Car Ownership Model for Great Britain, 2011 Base” (RAND Europe, 2016) 
4 Conducted as part of the DfT’s “Understanding and Valuing Impacts of Transport Investment” research programme 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
9
 
Geography 
1.3.3. The NTEM zone is the main spatial unit in NTEM. NTEM zones for England & Wales are consistent 
with Middle Super Output Areas (MSOAs), whilst for Scotland, NTEM zones are an aggregation of Data Zones 
(DZs). 
1.3.4. Earlier versions of NTEM (up to and including v6.2) includes Scotland at the same geographical level 
as England as Wales. However, since the formation of this dataset, significant devolution has occurred in 
Scotland, with consequences for the implementation of NTEM 7. The 2011 Census, on which the majority of 
input data is based, was conducted separately for Scotland. As a consequence, different definitions for 
geography and input data requirements are in place. 
1.3.5. Moving to MSOA based geography significantly increases the number of NTEM zones. Where 
previously there were under 2,500 for the entirety of Great Britain, the number of NTEM 7 zones for England 
& Wales alone is 7,201. This has not caused any problems with construction of the model or software, and 
does not present any additional complexity for purposes of quality assurance.  
1.3.6. NTEM 7 zones in Scotland have been formed as an aggregation of Data Zones. A total of 499 zones 
have been established, equivalent to the coverage provided in England and Wales.  
1.3.7. In combination, there are now 7,700 NTEM 7 Zones in Great Britain. Table 1-1 shows a comparison 
between NTEM 6.2 and 7 zoning systems. 
 
Table 1-1: NTEM zone system comparison 
Area 
Number of NTEM zones (6.2) 
Number of NTEM zones (7) 
East of England 
295 
736 
East Midlands 
260 
573 
London 
57 
983 
North East 
132 
340 
North West 
282 
924 
Scotland 
264 
499 
South East 
410 
1,108 
South West 
244 
700 
Wales 
179 
410 
West Midlands 
168 
735 
Yorkshire and Humber 
205 
692 
Great Britain 
2,496 
7,700 
 
Data segmentation  
1.3.8. The NTEM suite contains a variety of age bands for use in demographic projections and trip end 
modelling. Due to general changes in Census definitions, there has been an expansion of the ‘working age’ 
category. This is displayed as ‘Planning data’ in TEMPRO, as shown in Table 1-2. 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
10
 
Table 1-2: NTEM age definitions 
NTEM 6.2 
NTEM 7 
< 16 
< 16 
16 to 64 
16 to 74 
65+ 
75+ 
 
1.3.9. In addition, the age bands used in the Scenario Generator process were also disaggregated. The 
previous two working age bands (16-29, 30-64), have been amended to four working age bands (16-29, 30-
44, 45-64, 65-74). This enables better reflection of changing age structure in the population. This is explained 
further in Section 3 of this document. 
Source data 
1.3.10. This version of NTEM has a base year of 2011, requiring extensive use of the 2011 Census of 
Population and re-formulation of base and forecast parameters. 
1.3.11. Reflecting the change of base year, the ultimate time horizon of the model has been extended from 
2041 to 2051. The model continues to be run at five year increments, with results interpolated for intervening 
years in the TEMPRO software. 
1.3.12. Forecast data has also been updated, as described in Section 4 of this report. 
1.3.13. All parameters, EGFs and balancing areas have been updated to reflect new source datasets. 
1.3.14. The forecasting methodology for the Planning Data has not been revised. The National Car Ownership 
model has been re-estimated with 2011 data, and trip rates by person type have been re-estimated for use in 
CTripEnd from an updated NTS sample.  
 
 
 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
11
 
2. 
Planning data methodology 
2.1. 
Summary 
2.1.1. The Scenario Generator is a software tool that produces planning data sets by using base and forecast 
data supplied by the user at various levels of geographical and other detail. 
2.1.2. The Scenario Generator used for NTEM 7 has been designed to run with a zoning system based on 
2011 Census geography to produce demographic forecasts at five year intervals from 2011 to 2051. The 
software is flexible enough to allow forecasting for any required period, and at any spatial level given the 
availability of adequate input data. 
2.1.3. This section outlines the steps that the Scenario Generator performs to produce a consistent set of 
planning data forecasts.  
2.2. 
Objective 
2.2.1. The objective of the Scenario Generator is to produce a set of consistent forecasts of population, 
households and employment for a pre-defined set of zones. The planning data forecasts are obtained by 
scaling up or down a set of previous zone specific forecasts to match a new set of control totals for more 
aggregate areas that are input by the user. Pre-processing of forecast data is required to obtain the initial input 
control totals. 
2.2.2. Each of the main variables are segmented into categories as follows: 
 
Employment: by industry, gender and working status (full-time / part-time); 
 
Population: by gender, age and working status; and 
 
Households: by size. 
2.2.3. Figure 2-1 shows a basic summary of the steps in producing forecast planning data at the NTEM zone 
level. The fundamental steps are as follows: 
 
Derive detailed demographic data for the starting year (either the defined base year or an already 
generated forecast year); 
 
Derive forecast year data from input planning projections or policy-based datasets; 
 
Use expected growth factors to assign change in control area totals to the NTEM zone level; 
 
Balance workforce (required for jobs) and workers (employed people in households) within a 
balancing area; 
 
Update worker profile for changes in employment forecasts; and 
 
Output planning data for input into the National Car Ownership (NATCOP) model and the Trip 
End (CTripEnd) model. 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
12
 
Figure 2-1: Summary of Scenario Generator process 
 
 
2.2.4. The level of aggregation of the resulting planning data forecasts is dependent on the ultimate use, with 
fewer categories required for the National Car Ownership Model. Demographic forecasts are exported from 
the Scenario Generator in the precise form required by these models. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
13
 
2.3. 
Basic inputs and spatial definitions 
Study areas 
2.3.1. The methodology assumes that planning data forecasts for each study area will be wholly independent 
of those for other study areas in Great Britain. Study areas have been defined as the Government Office 
Regions in England, plus Scotland and Wales. One exception is for the South East, East of England and 
London Regions. These three are considered as one study area (Wider South East) as London has a large 
impact on working and commuting patterns on the surrounding areas. The user may generate forecasts for 
any one of the specified study areas. This allows separate updates to be made for each study area where 
specific forecast planning data may be obtained in the future. 
Control areas 
2.3.2. Within each study area, forecast data from various projections or policy-based sources are likely to be 
provided at a spatial level between NTEM zone and the study area as a whole. These are defined prior to 
using the Scenario Generator and are referred to as control areas. This allows the software to bridge the gap 
between NTEM zones and the study area as a whole. In principle, control areas could be any level of spatial 
aggregation that sits between the NTEM zone definitions and the study area definition: 
 
NTEM ZONES  CONTROL AREAS  STUDY AREA  GREAT BRITAIN 
2.3.3. Two control area zoning systems exist for the main data types: 
 
Population-based (population, households and dwellings); and 
 
Employment-based (workers and jobs). 
2.3.4. However in NTEM 7 the population and employment based control areas are identical. The population-
based and the employment-based control areas are both at the local authority district level. This is consistent 
with the previous version of NTEM (6.2). 
Basic input data 
2.3.5. There are three main planning data inputs of the forecasting process: households, population and 
employment (jobs). The input data is typically government provided trend based projections which may be 
modified during the process of developing a coherent set of planning led forecasts. The input variables are as 
follows: 
 
Households by size in the study area for each forecast year; 
 
Population by gender and age for each control area in each forecast year; and 
 
Employment (jobs) by industry, gender and working status in the control area for each forecast 
year. 
2.3.6. The policy based inputs which influence the allocation of population and workers in the forecasting 
process are determined from projections of dwellings and growth factors that represent expected patterns of 
development. 
 
Dwellings in each control area; 
 
Expected growth factors in each zone for employment type; and 
 
Expected growth factors in each zone for households (all types). 
2.3.7. All working data is adjusted to mid-year estimates to avoid issues of under-enumeration that may be 
evident from basic 2011 Census data. The mid-year estimation process is discussed in Section 3 
2.3.8. In addition to the basic input data, several base and forecast control parameters are required in each 
forecast run. These parameters are generally global in nature, controlling the performance of the model. The 
control parameters are described in more detail in Appendix C. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
14
 
Population and household totals 
2.3.9. Population projections input data requires an adjustment to account for those persons not living in 
households who are excluded from the forecasting process. These are people who live / work in communal 
establishments which include residential care homes, student halls of residence, military barracks and prisons. 
2.3.10. According to the 2011 Census, 1.7% of the entire population of Great Britain is not resident in a 
household. Further analysis of the data demonstrates that the proportion of the population in communal 
establishments varies by age and to some extent by gender. The largest groups of the population not resident 
in a household are students between 16-29 and elderly persons aged 75 and over. 
2.3.11. There are also variations by gender. This is apparent for the bulk of the adult population (e.g. 30-64) 
where a higher proportion of males are resident in communal establishments and also the elderly population 
(75+) where there is a greater proportion of females. 
2.3.12. There is limited variation in the proportions of communal residents in the different regions of Great 
Britain. The set of factors applied are described in Appendix C. 
2.3.13. The household totals input are taken directly from government projections as the totals for each study 
area and summed over household types. This initial estimate is checked against input dwellings figures and 
may be subsequently adjusted. 
Jobs and workers totals 
2.3.14. Total number of jobs (employment) at control area level in each forecast year are provided as an input. 
This information is used to scale the jobs from the previous scenario which are then aggregated to give study 
area totals.  
2.3.15. The number of workers are required in order to relate the employment data measured in units of jobs, 
to the population data measured in units of persons in employment. A set of factors is derived to convert the 
numbers of jobs to the numbers of workers. These are described in Appendix C. 
Control total checks 
2.3.16. Checks are made by the Scenario Generator on the total number of households, people, workers and 
jobs, to ensure that they are logically coherent before they are disaggregated both spatially and into the 
categories required for the car ownership and trip end models. The user imposes the acceptable boundary 
that each variable should fall within. 
2.3.17. Table 2-1 outlines the control total ratios used in the Scenario Generator, including the default limits 
set in the software. These are all checked for consistency at the study area level. 
Table 2-1: Control total checks in Scenario Generator processing 
Ratio 
Lower limit 
Upper limit 
Average adults per household 
1 
2.5 
Workers to population (employment rate) by gender and 
age group 
50% in general 
90% in general 
Households to dwellings / household spaces (occupancy 
and vacancy rates) 
-10% 
10% 
Workers to jobs 
No limit, but output to log 
 
2.3.18. The results of these checks are written to the log file generated for each run. In the case that a certain 
limit is infringed, the tool will report this and in most circumstances the run will cease. This ensures that the 
basic input data is robust enough to produce realistic forecasts. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
15
 
2.4. 
Households by control area 
2.4.1. The allocation of population and households to control areas starts with trend based forecasts and 
then (if policy based forecasts are used) adjusts these to reflect the availability of dwellings.  
Population – trend based estimation 
2.4.2. The first step is to disaggregate the study area household projections using input population projections 
for each control area. This trend based process assumes the change in household size is uniform for all control 
areas within a study area, however the starting pattern of household size is retained. The result is the first 
estimate of household demand for each control area. 
2.4.3. The number of households in the control area is split into one person and two or more person 
households. Due to changing household sizes over time it is not possible to take the ‘previous’ split between 
1 and 2+ person households in each control area. The overall change in household size for each study area 
is known from the input data. Using this information the average size of the 2+ person households in each 
control area can be scaled back. Since some control areas have relatively low household sizes to start with, it 
is important to check that the resulting number of people per 2+ person households remains above a specified 
lower limit. 
2.4.4. If the household size of a control area reaches the lower limit, household sizes in other control areas 
will need to be readjusted to retain the study area control total. This process iterates until stable, providing the 
trend-based estimate of households by type. 
2.4.5. Figure 2-2 outlines the process of obtaining the first estimate of households (by type) in each control 
area based on change in population. 
Figure 2-2: Household estimation based on control area population changes 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
16
 
Dwelling supply – policy based estimation 
2.4.6. Forecast year households based on the change in dwellings are also estimated for each control area. 
This is because in reality the choice of household location in the future is often affected by dwelling supply. It 
is assumed that household and dwelling growth are moving in the same direction i.e. both are growing or both 
are declining. If this is not the case, household estimates are based purely on population trends as calculated 
in the previous step for a given control area. 
2.4.7. To implement this step, the change in households specified by the user at the regional level is 
disaggregated into control areas. This is achieved by using the proportion of forecast year dwelling growth in 
a control area relative to the regional total. 
Combing trend and policy based estimates 
2.4.8. In practice the pattern of households is likely to be influenced by a mixture of trends and new 
developments. Thus a combination of the population-led and dwellings-led approaches are taken. The extent 
to which household forecasts are policy led is defined by a user specified parameter. A value of 0.75 was 
adopted to produce the NTEM forecast planning dataset, erring more towards policy based forecasts than past 
trends. If this parameter were set to zero the forecasts would be wholly dependent on population trends, whilst 
a value of one indicates that only dwelling supply contributes to the estimate of households. This is consistent 
with that previously used in NTEM v6.2. 
2.4.9. It will often be the case that within a control area housing demand cannot be accommodated by the 
supply of dwellings. In this case, the Scenario Generator identifies excess households in each control area. 
Excess households may be suppressed or, if capacity exists elsewhere in the study area, a proportion of them 
may be redistributed to other control areas in the study area.  
2.4.10. The potential supply of housing within a control area is based on the number of available household 
spaces. This is derived mainly from the number of dwellings allocated to each control area. Other factors that 
are considered in each control area are as follows: 
 
Occupancy rate (number of households per occupied dwelling); and 
 
Vacancy rate (number of presently uninhabited (long term vacant dwellings). 
2.4.11. Controls are placed on vacancy and occupancy rates so that these do not change radically over a 
forecast period. These may also be controlled by the user. Occupancy rates may not change by more than 
0.5% in a forecast period. Vacancy rates may not reduce by more than 25% of their previous value. Constraints 
are also placed on these variables to maintain a realistic housing profile. 
2.4.12. Once calculated, the estimated number of households required in each control area is compared to 
available household spaces. Where this proportion is less than one, there is enough capacity for households 
to reside within a given control area. Where the proportion is greater than one, there is an excess of households 
within a given control area and they are subsequently relocated, suppressed or a combination of both.   
2.4.13. The suppression of households is expected to alter the mix of household types in the study area due 
to the different reaction that different households types will have to limited housing capacity. The household 
type profile of those households being reallocated is assumed to be in line with the control area profile from 
which the households are moving. The required suppression is a representation of households coalescing or 
a reduction in household formation. This is more likely to impact on one person households than multi-person 
households - e.g. young people staying at home longer or grouping together to form multi-person households 
as they are unable to afford their own place of residence.   
2.4.14. Two key user defined parameters are required here: 
 
The proportion of excess households that are willing to relocate to other control areas in the study 
area (default=0.5 for each time period); and 
 
The number of one-person households suppressed per 2+ person households formed 
(default=3). 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
17
 
2.4.15. Where households wish to relocate, they are redistributed among the remaining control areas within 
the study area that have capacity. In the rare case that total capacity in a study area is exceeded, the remaining 
households are suppressed. Households that were unable to initially be allocated are distributed among other 
control areas on a pro-rata basis relative to the proportion of capacity within a study area. 
2.4.16. Figure 2-3 outlines the process of allocating households to control areas. 
 
Figure 2-3: Allocation of households to control area 
 
 
2.5. 
Population by control area 
2.5.1. The trend based forecast population is an input for each control area. Household forecasts are adjusted 
to take account of these population trends, the expected change in dwellings and to alleviate housing pressure. 
Since there is some reallocation of households amongst control areas within a study area to take account of 
planning policy, then there should be an associated reallocation of population. 
2.5.2. Input population projections are adjusted to reflect policy based household adjustments. An estimate 
of the number of people (by type) in each control area to be reallocated is calculated from the initial household 
estimate and the final household figures (by type), taking into account the population profile (persons per 
household) in each household type. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
18
 
2.5.3. Where there is a reduction in the number of households between the trend and policy based forecasts, 
the population is reduced based on the persons per household in that control area. For areas gaining 
households, the associated population is determined from the average population profile of all the relocating 
households. 
2.6. 
Population and households by zone 
2.6.1. Households remain the main allocation variable based on expected patterns of trends in development. 
The number of households in each control area are thus distributed to NTEM zones within a control area based 
on expected growth factors (EGFs), as discussed in Section 5. From the zonal household totals, population 
totals by NTEM zone may be derived using the numbers of people expected in each household type. Figure 
2-4 shows a diagrammatic view of this process. 
Figure 2-4: Derivation of households and population by zone 
 
 
2.6.2. EGFs for households are applied to households by NTEM zone from the previous scenario to form a 
profile of household growth for each control area. Where growth factors are applied it is possible that in certain 
areas households by type become negative where areas are in decline. Where this occurs adjustment is 
required to cap each case so that they cannot fall below zero. Where this does occur, the total that is adjusted 
back must be applied to all other zones pro-rata based on their relative EGF. This is an iterative process. Once 
stable, it is applied to ensure that the resulting number of households in each NTEM zone matches the number 
of households that would be obtained by applying the adjusted growth factors to the previous household total. 
2.6.3. Population estimates by NTEM zone may be derived from households by application of the expected 
number of people in each household type (using those adjusted for the new population profile after the 
reallocation of households). In addition to disaggregated zonal population totals from the previous scenario, 
this produces initial estimates of zonal population by age and gender. These are then controlled to the user 
input population totals for control areas on a pro-rata basis. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
19
 
2.7. 
Employment and workers by zone 
2.7.1. The allocation of employment from control areas to NTEM zones is carried out using one of three 
approaches: 
 
An endogenous approach based on household forecasts (e.g. schools); 
 
Employment is assumed to follow trends in exogenous industrial sectors; and 
 
Insufficient data exists and growth is assumed to be based on previous totals. 
2.7.2. The allocation of the population led employment is carried out using the projected change in 
households by NTEM zone. This is then adjusted to match the control totals for the employment sector within 
the control area. 
2.7.3. The allocation of the remaining employment sectors (non-population led) is based on a set of growth 
factors which are exogenously defined. These are calculated by applying growth factors for each employment 
sector and then controlling to expected change in employment for each control area. The expected change in 
employment is the previous year’s employment factored up by the input forecast data at the control area level. 
2.8. 
Balance workforce and workers 
2.8.1. The Scenario Generator estimates the future year workforce required to fill the input levels of future 
employment (jobs). The workforce forecast then determines how many people in the population are employed. 
The workforce (at the workplace) is balanced with the employed residents (at home locations). These 
Balancing Areas do not mean the area is closed with everyone working within the area also living there. Instead 
they are areas which are reasonably closed and have a similar number of people commuting out from the 
area, as commuting in to work. 
2.8.2. This process ensures that the proportion of workers that live and work within the same area are in 
balance. This makes use of the balancing area geography that may be seen in Appendix A. The balancing 
process is concerned primarily with the ‘working age’ population (i.e. 16-29, 30-44, 45-64 and 65-74 age 
groups). The balancing process ensures that: 
 
There are the correct number of workers of different types living in the balancing area for the jobs 
available; and 
 
The number of workers in each zone does not exceed a specified maximum proportion of the 
resident population. 
2.8.3. These two steps are addressed in turn. Firstly by checking and if necessary adjusting the gender / 
working status profile for the required workers in the balancing area, and secondly reallocating the workers 
amongst the NTEM zones within the balancing area to ensure no zone breaches the ceiling employment rate. 
2.8.4. The number of workers required in each balancing area by age category are obtained by converting 
employment forecasts (jobs) to the number of workers, and then splitting workers into the working age bands 
(16-29, 30-44, 45-64 and 65-74). This makes use of two important sets of user-defined parameters that: 
 
Convert jobs to workers by gender, work type (full time/ part time) and employment sector using 
the Regional profile; and 
 
Divide workers into the necessary working age groups using the regional profile provided by the 
base year Census data. 
2.8.5. This internal adjustment process uses the following key concepts: 
 
Treating the previous population and the new (migrating) population as two separate groups; 
 
Treating declines before and completely independently to growth – logically they can only be 
applied to the ‘previous’ population groups; 
 
Using ‘existing’ profiles / employment rates from the previous scenario as an initial estimate of 
workers; 
 
Calculating maximum possible numbers of workers by type; 
 
Adjusting demand profile of workers so no maxima are breached; and 
 
Determining how far it is necessary to move from the initial values towards the maximum values 
in order to satisfy demand. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
20
 
Reduction in population / workers 
2.8.6. If the population of an NTEM zone within a balancing area is forecast to decline, then this would 
naturally lead to a reduction in the number of workers in that zone irrespective of whether the required 
workforce in the balancing area is forecast to decline or grow. In addition, the workforce required in the 
balancing area might be forecast to fall more or less than the reduction caused by the population change, or 
alternatively to increase from the previous scenario. This gives two cases which must be considered 
2.8.7. The first stage is to account for any reduction in workers due to a reduction in the population. Where 
this occurs, the population in full-time and part-time work is reduced according to the current proportions of 
each. 
2.8.8. The second stage is required only if the workforce in the balancing area is forecast to decrease in any 
work type, gender or age category. In this case the number of workers is reduced while retaining the total 
forecast population numbers by NTEM zone. This is achieved by simply scaling back the number of workers 
(by working status) in each zone to match the control area total. The number of non-workers are then increased 
proportionally by the same volume to retain zonal population totals.   
2.8.9. Figure 2-5 outlines the process in calculating an estimate of population based on potential reductions 
in zonal populations or worker types within a balancing area. 
 
Figure 2-5: Estimation of zonal population 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
21
 
Initial estimates of working status profile of zonal population 
2.8.10. It is considered that the working status profile of large parts of the resident population will not alter 
significantly through time, while new developments that lead to significant growth in an area might have very 
different profiles to the existing population. The population is therefore separated into two groups: 
 
The existing residents from the previous year / scenario with any declines in population already 
subtracted – a group for which the working status is known; and 
 
Any increase in population in the forecast year for which the working status has yet to be forecast. 
2.8.11. An initial estimate of the population profile can be obtained by assuming the ‘new’ population takes on 
the same profile as the existing population. 
Maximum supply of workers by gender 
2.8.12. The maximum supply of workers by gender in a forecast year is calculated. It is derived by applying 
the maximum acceptable change over the forecast period to the previous scenario. This rate is user defined 
and defaults to +5% (i.e. employment rate could increase from 65% to 70%). A ceiling rate is also set and 
when exceeded will cap the maximum worker supply at this level. Maximum employment rate is set for each 
age and gender combination. A ceiling rate is applied to both full-time and part-time workers, as well as a 
maximum rate for both types combined. The Scenario Generator ensures that neither limit is breached. 
2.8.13. Since the maximum employment rate has been ascertained, the maximum supply of workers per 
NTEM zone may be calculated. Working age population in the previous year may in the forecast year be 
capable of working to the higher employment rate. In addition, new working age population introduced through 
demographic change in the forecast year may introduce new workers, with the maximum capped to the same 
employment rate. This provides the upper bounds for the number of workers by age and gender. 
2.8.14. Figure 2-6 outlines the process in deriving the maximum number of workers in each NTEM zone by 
age, gender and employment type. 
 
Figure 2-6: Derivation of the maximum number of workers in each NTEM zone by person type 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
22
 
Checking / adjusting the gender profile of worker demand 
2.8.15. On comparing the initial estimates of worker demand within a balancing area to the maximum available 
working population, it is possible that in some cases this will be exceeded for at least one combination of age 
and gender. If for example the demand for male workers cannot be met by the maximum available male 
workers in the population within a balancing area, these posts are transferred to female workers if there is 
availability in the female population. 
2.8.16. There are three scenarios considered for each working age band within a balancing area: 
 
There is no excess in demand for workers of either gender; 
 
There is excess demand for workers of both genders; or 
 
There is excess demand for workers of one gender but not the other. 
2.8.17. Where there is no excess demand for workers in a balancing area, the maximum available workers in 
the population may accommodate the labour demand. Therefore no adjustment is necessary. 
2.8.18. Where there is excess labour demand for both male and female workers, there is no capacity to 
reallocate these workers across genders. Therefore this excess is simply written off so that the maximum 
number of workers does not exceed the maximum possible working population. This effectively assumes that 
posts will be filled by an increase in double jobbing and the numbers of workers aged 75 and over. 
2.8.19. Where demand for workers in one gender exceeds supply, there is scope for the demand to be met by 
workers of the opposite gender. This of course changes the gender profile of the working population within the 
balancing area. This is achieved by allocating workers from one gender type to the other. In the event that 
there is insufficient capacity in the other gender to meet the whole demand, this residual excess is written off. 
2.8.20. Figure 2-7 highlights the allocation process in distributing workers within each balancing area by age 
and gender. It may help understanding by assuming that in this stage, workers by age category are effectively 
handled separately. 
Figure 2-7: Allocation of workers by age and gender in a balancing area 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
23
 
Checking / adjusting the full-time / part-time split of workers 
2.8.21. At this stage, workers by age and gender have been resolved, removing any excess workers that 
cannot be allocated without exceeding the maximum imposed limits. To give a final forecast of the number of 
workers in each balancing area, the split between full-time and part-time workers needs to be resolved. 
2.8.22. For each age and gender combination, the maximum number of available full-time and part-time 
workers is fixed by the user specified maximum change in employment rate from the previous scenario (5% 
by default). The Scenario Generator checks whether the number of estimated workers falls within these 
acceptable limits for each balancing area within the study area.  
2.8.23. If full-time and part-time workers may be accommodated, no adjustment is required. It is not possible 
for both rates to be exceeded since all potential excess has been removed in the previous step (adjustment of 
the gender profile). 
2.8.24. If there is an excess of one working status, those extra workers are switched to the other working status 
(e.g. where no more full-time workers are permitted, the excess will be accounted for by increasing the number 
of part-time workers). 
2.8.25. This gives a final forecast of workers in each balancing area by age, gender and working status. From 
these, new employment rates may be derived and are output from the Scenario Generator in the log file. The 
last step is to allocate workers to zones. 
Allocation of workers to zones 
2.8.26. The allocation of the adjusted and balanced workers to individual NTEM zones is carried out by looking 
at the difference between the maximum and initial zonal estimates for the numbers of workers and determining 
what proportion of this difference is needed to achieve the required demand figures. 
2.8.27. There are two possible scenarios: 
 
Workforce demand is a greater proportion of the population than in the previous scenario 
(expected to be the norm); or 
 
Workforce demand is a smaller proportion of the population than in the previous scenario. 
2.8.28. If the proportion of the population required to work is less than in the previous scenario, the initial 
estimates by zone are simply scaled back based on the difference at the balancing area level. 
2.8.29. If the proportion of workers has increased then the employment rate lies somewhere between the initial 
and the maximum employment rate. Where this is the case, each zone requires the working population to be 
scaled up accordingly. An adjustment factor is derived for the balancing area according to the relative demand 
for workers between the initial working population estimate and the maximum number of workers possible in 
the balancing area. This factor is used to scale each zone and controls total workers across all zones to the 
balancing area total. 
2.8.30. Figure 2-8 outlines the process of allocating workers from Balancing Areas to NTEM zones. 
Non-workers by zone 
2.8.31. What remains is the allocation of non-workers to NTEM zones. This places the remaining working age 
population into the student or other categories. These are derived from zonal population and workers and the 
proportion of non-worker types in the previous scenario. This means that the number of students will vary by 
scenario. 
2.8.32. In rare cases where no non-workers exist in the previous scenario, yet allocation of non-workers is 
required, the assumption is made that all will be allocated to the ‘other’ category. The assumption is that the 
other category is more ubiquitous than the student category and avoids assigning students in areas potentially 
a long distance from, for example, higher education. In practise these values will be very small. 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
24
 
Figure 2-8: Allocating workers from balancing area to zones 
 
 
2.9. 
Update employment forecasts for change in worker profile 
2.9.1. The required number of workers by gender, age group and working status is obtained from the number 
of jobs input data. The demand for workers may not be achievable in population forecasts. The Scenario 
Generator internally adjusts the gender, working status and age profile of the required workforce in order to 
balance the demand and supply of workers. 
2.9.2. Since employment forecasts are an input in future scenarios, any adjustments made to the profile of 
the working population is carried through to adjust job profiles. This update can be carried out as a final step 
in the estimation process, solely to provide an improved base from which future scenarios are forecast. 
2.10. 
Summary of outputs from the Scenario Generator 
2.10.1. The Scenario Generator produces three main output files: 
 
A log file for the scenario(s) generated; 
 
Population and household forecasts for the National Car Ownership Model; 
 
Population, household and employment forecasts for the National Trip End Model; and 
 
Population and economic activity forecasts for trip rate modelling. 
2.10.2. The log file summarises the performance and key variables associated with each scenario run. The 
log informs the user of the successfulness of the run and any errors that may have occurred, including warnings 
where data consistency issues may need to be resolved. For information purposes, the log file also 
summarises the key elements of a scenario run: 
 
Input parameter settings; 
 
Checks on control totals; 
 
Allocation and suppression of households; 
 
Allocation of population; and 
 
Derived control area vacancy rates. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
25
 
2.10.3. The planning data forecasts for the National Car Ownership Model are output in the appropriate text 
file format. These are: 
 
Total population in NTEM zones; and 
 
Persons by gender, age and working status. 
2.10.4. Planning forecasts for the National Trip End Model are output in the appropriate database format. 
These are: 
 
Employment forecasts by zone; 
 
Number of jobs; 
 
Number of households; 
 
Number of jobs by sector; 
 
Population forecasts by zone; and 
 
Population in NTEM Zones by gender, age and working status. 
2.10.5. An additional table in the database output provides information relating to all person types, which is 
used as an input to trip rate forecasting. 
2.10.6. In addition to these outputs, the results from the forecasting process are stored in data tables within 
the Scenario Generator database. 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
26
 
3. 
2011 base year data 
3.1. 
Summary  
3.1.1. The base year for the planning data in NTEM 7 is 2011. This data is primarily obtained from the 2011 
Census and then adjusted to 2011 mid-year estimates in order to counter the effects of potential under-
enumeration and to ensure consistency with mid-year based projections used for forecasting. 
3.1.2. The following datasets constitute the base year inputs: 
 
Population (age, gender and working status in each NTEM zone); 
 
Households (size in each NTEM zone); 
 
Dwellings (quantity in each control area); 
 
Employment (quantity of jobs by industry, gender and working status in each NTEM zone); and 
 
2011 mid-year estimates from projection data (by control area). 
3.1.3. Several additional parameters are required as part of the base data input: 
 
Expected number of people in each household type; 
 
Maximum acceptable difference between households and dwellings; 
 
Minimum number of average adults per household; 
 
Maximum number of average adults per household; 
 
Occupancy rate for dwellings; 
 
Proportion of population in communal establishments; 
 
Dwelling vacancy rate; 
 
Number of dwellings not used as main residence; 
 
Ratio of jobs to workers; 
 
Minimum and maximum acceptable employment rate and change in employment rate over time; 
 
Maximum allowable change in occupancy rate; 
 
Proportion of workers in each working age group; 
 
Proportion of excess households that relocate; 
 
Weighting of population / dwelling policy-led allocation of households; and, 
 
Number of suppressed one person households that are reformed as multi-person households. 
3.1.4. Base year data is required at the NTEM zone level. England & Wales NTEM zones are identical to 
Middle Super Output Areas (MSOAs), whilst NTEM zones for Scotland comprise of aggregations of Data 
Zones (DZ). 
3.1.5. This section describes the basic data required in the base year. Where Census tables are not available 
at MSOA (England & Wales) or DZ (Scotland) level, data at a more coarse geographical level has been utilised, 
and methods were adopted to convert this data to NTEM zone level. Those assumptions are outlined in this 
section. 
3.2. 
Population 
3.2.1. Base year population is required by gender, age and working status for each NTEM zone. These are 
broken down into the following categories: 
 
Gender: male, female; 
 
Age: children (0-15), working age (16-29, 30-44, 45-64 & 65-74), elderly (75+); 
 
Working status: full time employed, part time employed, full time students, other. 
3.2.2. Children and those over the age of 74 are not differentiated by working status. This effectively gives 
36 categories of population for each zone (see Appendix B for detailed segmentation definitions). 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
27
 
3.2.3. Table 3-1 lists the data sources used in the derivation of base population data. All data sources used 
in deriving the base population are from the 2011 Census. No individual datasets achieve the level of 
segmentation required, therefore it was necessary to manipulate a combination of datasets in order to produce 
the base population input for NTEM 7. Categories and definitions do vary between source datasets, thus it 
was imperative to take this into consideration whilst manipulating the source base population data. 
Table 3-1: Base year population data sources 
Source 
Geography 
Description 
Census Table DC6107EW 
MSOA 
All usual residents In England & Wales aged 16+ by 
economic activity, age and gender. 
Census Table DC1117EW 
MSOA 
All usual residents In England & Wales by age and 
gender. 
Census Table LC1117EW 
LAD 
All usual residents In England & Wales by age and 
gender. 
Mid-2011 Population 
Estimates 
LAD 
2011 Mid-year population estimates for local 
authorities in England and Wales by age and 
gender. 
Census Table LC1105EW 
MSOA 
All usual residents by residence type in England & 
Wales. 
Census Table LC6107SC 
DZ 
All usual residents in Scotland aged 16+ by 
economic activity, age and gender. 
Census Table DC6107SC 
Council Area 
All usual residents in Scotland aged 16+ by 
economic activity, age and gender. 
Census Table LC1117SC 
DZ 
All usual residents In Scotland by age and gender. 
 
Census Table DC1117SC 
Council Area 
All usual residents In Scotland by age and gender. 
 
Mid-2011 Population 
Estimates 
Council Area 
2011 Mid-year population estimates for Council 
Areas in Scotland by age and gender. 
Census Table LC1104SC 
DZ 
All usual residents by residence type in Scotland. 
3.2.4. The 2011 Census data has been adjusted to 2011 mid-year estimates. The rationale for mid-year 
adjustments is to overcome potential under-enumeration of data collected from the 2011 Census and to ensure 
consistency with the mid-year based population projections. This adjustment is relevant for all of the main base 
year variables. 
3.2.5. The 2011 mid-year population estimates were obtained from ONS for England & Wales and Scotland. 
The dataset was used to produce a growth factor between the 2011 mid-year population estimates and age 
categories from the 2011 Census tables DC1117EW and DC1117SC. This provides a set of scaling factors for 
gender and age group at LAD level in England & Wales and Council Area level in Scotland. These scaling 
factors are then used to adjust the multi-dimensional 2011 Census population data to 2011 mid-year levels. 
There is a slight difference in age definitions between the two sources (i.e. mid-year estimates have a 0-14 
age category rather than 0-15). The scaling method takes account of this. 
3.2.6. As NTEM is a household-based model, communal residents are removed from the base population 
data in NTEM. The final step in processing the base population data was to apply a factor to remove communal 
residents from the dataset. Factors were derived by calculating the proportion of communal residents within 
the population by age group at the NTEM zone level. Census tables LC1105EW & LC1104SC were used to 
derive these factors. 
3.2.7. Table 3-2 shows the total household population of Great Britain in the base year, divided into the 
segmentations required for the Scenario Generator. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
28
 
Table 3-2: Population by age, working status and gender (Great Britain, 2011) 
Age band 
Working  
status 
Female 
Male 
Total 
Proportion of 
population 
0 – 15 
All 
5,597,102 
5,867,952 
11,465,054 
19% 
16 – 29 
Full Time 
1,983,242 
2,627,086 
4,610,328 
8% 
16 – 29 
Part Time 
1,325,418 
845,096 
2,170,514 
4% 
16 – 29 
Student 
1,114,509 
1,204,135 
2,318,644 
4% 
16 – 29 
Other 
1,135,237 
929,507 
2,064,744 
3% 
30 – 44 
Full Time 
2,675,595 
4,780,460 
7,456,055 
12% 
30 – 44 
Part Time 
2,051,333 
526,074 
2,577,406 
4% 
30 – 44 
Student 
99,769 
59,901 
159,670 
0% 
30 – 44 
Other 
1,466,351 
810,547 
2,276,899 
4% 
45 – 64 
Full Time 
2,841,443 
5,126,984 
7,968,427 
13% 
45 – 64 
Part Time 
2,381,694 
734,306 
3,116,000 
5% 
45 – 64 
Student 
28,534 
16,020 
44,554 
0% 
45 – 64 
Other 
2,692,840 
1,839,541 
4,532,382 
7% 
65 – 74 
Full Time 
62,597 
190,267 
252,865 
0% 
65 – 74 
Part Time 
154,252 
158,812 
313,064 
1% 
65 – 74 
Student 
4,756 
4,507 
9,263 
0% 
65 – 74 
Other 
2,554,154 
2,200,702 
4,754,856 
8% 
75+ 
All 
2,645,662 
1,865,898 
4,511,559 
7% 
All 
All 
30,814,488 
29,787,795 
60,602,284 
100% 
 
3.3. 
Households 
3.3.1. Total households by NTEM zone are separated into one person households and households with two 
or more people. This dataset is also adjusted to mid-year estimates to be compatible with the rest of the 
planning data. Table 3-3 lists the data sources used in the derivation of base population data. 
Table 3-3: Base year household data sources 
Source 
Geography 
Description 
Census Table QS406EW 
MSOA 
Households by number of resident people. 
Census Table QS406SC 
DZ 
Households by number of resident people. 
Household Projections for 
England from DCLG 
LAD 
Household projections by number of resident 
people. 
Household Projections for 
Wales from StatsWales 
LAD 
Household projections by number of resident 
people. 
Household Projections for 
Scotland from NRS 
Council Area 
Household projections by number of resident 
people. 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
29
 
3.3.2. For England, and Wales, the numbers of 1 person and 2+ person households have been extracted 
from 2011 Census table QS406EW at MSOA level. These have been summed to LAD level and compared 
with the mid-year estimates, provided at LAD level, in the 2011-based household projections. Scaling factors 
have been derived for each LAD and household size, which have then been applied to the Census MSOA data 
to give mid-year values. 
3.3.3. For Scotland, the number of 1 person and 2+ person households have been extracted from 2011 
Census table QS406SC at DZ level. Mid-year estimates for all households in 2012 are provided at Council 
Area level in the 2012-based projections. A comparison between the 2011 Census total households for Council 
Areas have been compared with the 2012 mid-year estimates to calculate scaling factors to mid-2011 for each 
Council Area. These factors have been applied to the DZ level data to give mid-year values. 
3.3.4. Table 3-4 shows the total number of base year households by study area. 
Table 3-4: Study area households totals (Great Britain, 2011) 
Study area 
1 Person 
2+ Person 
Total 
East Midlands 
557,763 
1,339,682 
1,897,445 
North East 
365,473 
764,167 
1,129,639 
North West 
980,779 
2,030,483 
3,011,262 
Scotland 
824,258 
1,551,405 
2,375,663 
South West 
699,834 
1,570,274 
2,270,108 
Wales 
401,667 
902,158 
1,303,825 
West Midlands 
689,660 
1,608,290 
2,297,951 
Wider South East 
2,804,555 
6,466,820 
9,271,375 
Yorkshire & the Humber 
687,690 
1,538,410 
2,226,100 
Total 
8,011,680 
17,771,689 
25,783,369 
 
3.4. 
Dwellings 
3.4.1. Base year dwellings data has been taken from Census table QS418EW for England & Wales and the 
equivalent QS418SC table for Scotland (Table 3-4). Dwellings are required by control area.   
Table 3-5: Base year dwelling data sources 
Data source 
Geography 
Description 
Census Table QS418EW 
LAD 
Shared or unshared dwellings. 
Census Table QS418SC 
Council Area 
Shared or unshared dwellings. 
 
3.4.2. The dwelling numbers are adjusted to mid-year estimates using dwelling completion data from the 
Authority Monitoring Reports (formerly Annual Monitoring Reports). 
3.4.3. Table 3-6 shows the total number of base year dwellings by study area. 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
30
 
Table 3-6: Study area dwellings totals (Great Britain, 2011) 
Study area 
Dwellings 
East Midlands 
1,974,318 
North East 
1,179,238 
North West 
3,146,653 
Scotland 
2,504,397 
South West 
2,406,025 
Wales 
1,386,038 
West Midlands 
2,379,425 
Wider South East 
9,601,168 
Yorkshire & the Humber 
2,322,908 
Total 
26,900,168 
 
3.5. 
Employment  
3.5.1. The data required for the 2011 base year is the number of jobs (both the employed and self-employed) 
in each NTEM zone divided into forty-eight categories by: 
 
Employment sector (12 NTEM categories E03 to E14); 
 
Gender (males and females); and 
 
Working status (full-time / part-time). 
3.5.2. Table 3-7 shows the correspondence that has been defined between the NTEM employment types 
and SIC 2007 codes. 
Table 3-7: NTEM employment types 
NTEM employment type 
SIC (2007) code 
Description 
E03 
851, 852, 853 
Pre-primary, primary and secondary 
education 
E04 
854 
Higher education 
E05 
855, 856 
Adult and other education 
E06 
55 
Accommodation (hotels and campsites) 
E07 
451, 453, 471 – 478, 56103 
Retail trade 
E08 
86, 87 
Human health and residential care activities 
E09 
452, 454, 53, 6419, 683, 771, 
772, 75, 88 
Services (rental, repair, postal activities, real 
estate and other) 
E10 
05 – 09, 10 – 33, 35 – 39, 41 
– 43, 46, 49 – 52 
Industry, construction and transport 
E11 
56101, 56102, 563 
Restaurants and bars 
E12 
5914, 79901, 90 – 94 
Recreation and sport 
E13 
01, 02, 05 
Agriculture, fishing, forestry and logging 
E14 
All other codes 
Other employment 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
31
 
3.5.3. The task of processing base employment data is made more complex due to differing definitions of 
employment. Household based surveys typically measure the number of workers, whilst employer surveys 
provide information on the numbers of jobs. In some instances the two datasets differ quite markedly due to 
double jobbing and job sharing, in addition to differences caused by the survey design and samples surveyed 
(age categories etc.). 
3.5.4. The primary sources of employment data for NTEM are the 2011 Census of Population (workplace 
population) and the Workforce Jobs data series. 
3.5.5. Jobs are controlled to the national 2011 total from Workforce Jobs (WFJ). As NTEM is household 
based, this is scaled down to remove jobs not included in the NTEM definition (such as workers living in 
communal accommodation). This provides an NTEM 7 2011 total of jobs for Great Britain of 30.2m. This 
approach is consistent with that used in NTEM 6.2. 
3.5.6. In order to achieve the required granularity and segmentation required by NTEM, several other data 
sources are required, namely the Business Register and Employment Survey (BRES), which provides the 
breakdown according to industry and working status. Table 3-8 lists the data sources used in the derivation of 
base employment data. 
Table 3-8: Base year employment data sources 
Data source 
Geography 
Description of use 
Census tables WP605 
MSOA (England and Wales) 
Council Area (Scotland) 
Workplace population (by industry) 
BRES 
2001 MSOA / 2001 Data 
Zone 
Employment data by detailed 
geography, industry and working status 
Workforce Jobs: Total Jobs 
2011 
Region 
Provides constraint to total number of 
jobs by SIC category 
Workforce Jobs: Employees 
2011 
Region 
Allows gender split by region, broad 
industry and working status 
 
3.5.7. Using the WFJ derived total as the overriding constraint, 2011 Census tables provide estimates of 
workplace population. This is consistent with the approach taken in NTEM 6.2. BRES provides MSOA-level 
data for working status and to determine the split by NTEM employment type. As BRES has retained 2001 
Census geography definitions, conversion was required for consistency with the rest of NTEM. This includes 
merging and splitting of a small number of zones. 
 
 
 
 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
32
 
3.5.8. Table 3-9 shows total employment (jobs) associated with the household population in Great Britain in 
the base year, split by NTEM employment type. 
 
Table 3-9: Employment (jobs) by gender and working status (thousands, Great Britain, 2011) 
Employment type 
Female  
full-time 
Female  
part-time 
Male  
full-time 
Male  
part-time 
Total 
E03 Education (school) 
609 
974 
449 
230 
2,261 
E04 Education (higher) 
165 
128 
122 
30 
445 
E05 Education (adult/other) 
91 
84 
67 
20 
263 
E06 Accommodation 
117 
120 
152 
71 
460 
E07 Retail trade 
560 
1,273 
1,100 
580 
3,512 
E08 Health/Medical 
1,259 
1,097 
514 
156 
3,026 
E09 Services 
835 
657 
1,040 
202 
2,735 
E10 Industry/Construction/Transport 
886 
390 
5,211 
349 
6,836 
E11 Restaurants & bars 
217 
429 
287 
262 
1,196 
E12 Recreation & sport 
213 
298 
324 
211 
1,046 
E13 Agriculture & fishing 
39 
24 
193 
23 
279 
E14 Business 
2,182 
1,582 
3,694 
698 
8,157 
Total 
7,174 
7,056 
13,153 
2,834 
30,216 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
33
 
4. 
Forecast data and projections 
4.1. 
Summary  
4.1.1. For each study area and forecast year, the following datasets are required for any forecast year / 
scenario combination: 
 
Population projections (by age and gender) in each control area; 
 
Household projections (by size) for the study area; 
 
Forecast dwellings (in each control area); 
 
Employment projections (by sector, gender and working status in each control area); 
 
Zonal growth factors for employment in the period modelled (by sector); and 
 
Zonal growth factors for households in the period modelled. 
4.2. 
Population 
Data required 
4.2.1. Forecast year population data is required for each control area subdivided by gender and age group. 
The following groups are required: 
 
Children under 16; 
 
Females / Males 16-29; 
 
Females / Males 30-44; 
 
Females / Males 45-64; 
 
Females / Males 65-74; 
 
Females / Males 75-79; 
 
Females / Males 80-84; and 
 
Females / Males 85+. 
Population projections 
4.2.2. Population projections exist in two formats: national projections and sub-national projections. Since 
2006, the Office for National Statistics (ONS) has been responsible for producing both datasets. These 
datasets are the principal population projections from the ONS. NTEM 7 uses the latest mid-year based 
population projections, which were published in 2014 for England and Scotland, and 2013 for Wales. ONS 
only provide populations projections at control area level (sub-national) up until 2037, therefore national 
projections have been used to extrapolate beyond 2036 (forecast years 2041, 2046 and 2051).  
4.2.3. Table 4-1 indicates the year in which forecast population data has been derived at control area (sub-
national) and national level for England, Scotland and Wales. 
Table 4-1: Forecast population input data 
Area 
Geography 
Base year 
Data range 
England 
Control area 
2012 
2012 – 2037 
National 
2012 
2012 – 2087 
Scotland 
Control area 
2012 
2012 – 2037 
National 
2012 
2012 – 2087 
Wales 
Control area 
2011 
2011 – 2036 
National 
2012 
2012 – 2087 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
34
 
4.2.4. National level projections are used to extend the time horizon of the subnational data. Control area 
population forecasts were used up to 2036, whilst national level forecasts were used to continue control area 
trends beyond 2036 up until 2051. A factor based on national level population forecasts was applied to 2036 
control areas in order to maintain the regional proportions between control areas up until 2051. 
4.2.5. Table 4-2 shows a summary of the total population projections by year and study area input into the 
NTEM forecasting process. 
Table 4-2: Population projections by study area (thousands) 
Study area 
2016 
2021 
2026 
2031 
2036 
2041 
2046 
2051 
East Midlands 
4,682 
4,828 
4,971 
5,100 
5,209 
5,325 
5,438 
5,546 
North East 
2,632 
2,671 
2,708 
2,738 
2,762 
2,823 
2,883 
2,940 
North West 
7,182 
7,316 
7,439 
7,543 
7,629 
7,799 
7,964 
8,122 
Scotland 
5,386 
5,497 
5,606 
5,698 
5,768 
5,826 
5,877 
5,925 
South West 
5,483 
5,670 
5,855 
6,023 
6,167 
6,305 
6,438 
6,566 
Wales 
3,131 
3,199 
3,256 
3,300 
3,334 
3,359 
3,383 
3,405 
West Midlands 
5,764 
5,924 
6,074 
6,210 
6,333 
6,474 
6,611 
6,742 
Wider South East 
23,853 
24,997 
26,052 
27,009 
27,892 
28,513 
29,118 
29,694 
Yorkshire & the Humber 
5,424 
5,555 
5,682 
5,796 
5,894 
6,025 
6,153 
6,274 
Total 
63,537 
65,658 
67,643 
69,417 
70,988 
72,449 
73,866 
75,213 
 
4.3. 
Households 
4.3.1. NTEM 7 household projections for England and Wales are 2011 based, whilst projections for Scotland 
are 2012 based.  
4.3.2. Table 4-3 provides the source from which household projections have been downloaded. 
Table 4-3: Forecast year household data sources 
 
4.3.3. Mid-year adjustments are not required as all household projection datasets are already mid-year 
based. 
4.3.4. Household projections are required for five-year intervals from 2011 to 2051 by study area, subdivided 
into 1 person and 2+ person households.  
Data source 
Geography 
Description 
Household Projections for England 
from DCLG (2011 based) 
LAD 
Household projections by number of 
resident people. 
Household Projections for Wales from 
StatsWales (2011 based) 
LAD 
Household projections by number of 
resident people. 
Household Projections for Scotland 
from NRS (2012 based) 
Council Area 
Household projections by number of 
resident people. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
35
 
4.3.5. Household projections for England are available for 1 person and 2+ person households separately up 
to 2021 at LAD level. Projections for all households combined are available up to 2036 at LAD level. Between 
2021 and 2036, the trends in 1 person and 2+ person households have been extrapolated, with the total 
constrained to the combined projections at a regional level. Between 2036 and 2051, the trends in household 
size have been used to calculate households from the population projections. The same methodology has 
then been applied as for 2021 to 2036 to determine 1 person and 2+ person households. This is consistent 
with the approach adopted in NTEM 6.2. 
4.3.6. For Wales, projections for 1 person and 2+ person households are available up to 2036. Between 2036 
and 2051, the same methodology has been utilised as for England. 
4.3.7. For Scotland, projections for 1 person and 2+ person households are available for 2012 to 2037 at 5 
year intervals. Interpolation and extrapolation has been used to adjust these to the required years. Between 
2037 and 2051, the same methodology has been utilised as for England.   
4.3.8. The following tables show household projections (2016-2051) by study area for single person 
households (Table 4-4) and 2+ person households (Table 4-5). 
Table 4-4: Single person household projections by study area (thousands) 
Study area 
2016 
2021 
2026 
2031 
2036 
2041 
2046 
2051 
East Midlands 
582 
609 
633 
658 
679 
703 
727 
750 
North East 
377 
389 
400 
410 
419 
435 
451 
467 
North West 
1,013 
1,045 
1,075 
1,102 
1,126 
1,168 
1,209 
1,249 
Scotland 
901 
965 
1,027 
1,084 
1,138 
1,201 
1,258 
1,314 
South West 
727 
762 
792 
822 
849 
876 
903 
930 
Wales 
428 
456 
485 
510 
534 
557 
579 
602 
West Midlands 
716 
746 
774 
801 
825 
855 
884 
912 
Wider South East 
2,936 
3,088 
3,228 
3,365 
3,492 
3,592 
3,695 
3,799 
Yorkshire & the Humber 
715 
745 
773 
800 
825 
856 
886 
917 
Total 
8,395 
8,805 
9,186 
9,551 
9,888 
10,244 
10,593 
10,940 
 
Table 4-5: 2+ person household projections by study area (thousands) 
Study area 
2016 
2021 
2026 
2031 
2036 
2041 
2046 
2051 
East Midlands 
1,397 
1,453 
1,508 
1,562 
1,610 
1,663 
1,715 
1,766 
North East 
784 
806 
824 
842 
856 
887 
916 
945 
North West 
2,100 
2,171 
2,235 
2,295 
2,348 
2,438 
2,525 
2,612 
Scotland 
1,559 
1,582 
1,601 
1,618 
1,631 
1,644 
1,655 
1,666 
South West 
1,645 
1,717 
1,791 
1,862 
1,926 
1,990 
2,054 
2,117 
Wales 
921 
938 
950 
958 
960 
969 
974 
979 
West Midlands 
1,672 
1,738 
1,803 
1,865 
1,921 
1,989 
2,056 
2,122 
Wider South East 
6,917 
7,376 
7,833 
8,276 
8,694 
9,044 
9,399 
9,753 
Yorkshire & the Humber 
1,591 
1,645 
1,698 
1,748 
1,793 
1,852 
1,910 
1,967 
Total 
18,585 
19,426 
20,242 
21,025 
21,739 
22,475 
23,202 
23,927 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
36
 
4.4. 
Dwellings 
4.4.1. Dwelling stock values are combined with projected annual averages of housing completion from 2011 
onwards, in order to calculate the cumulative dwelling stock at Local Authority level between 2011 and 2051. 
4.4.2. Dwelling stocks were downloaded for England, Scotland and Wales individually: 
 
England: the 2011-2014 dwelling stock was extracted from ‘Dwelling stock: Number of Dwellings 
by Tenure and district’ published by the ONS; 
 
Scotland: the 2011-2014 dwelling stock was extracted from ‘Estimates of Households and 
Dwellings in Scotland’ published by the NRS; and 
 
Wales: the 2011 dwelling stock was extracted from the Census table ‘QS418EW’. Cumulative 
dwelling stock information was only available for 2011. 
4.4.3. Dwelling trajectories for England, Wales and Scotland were updated to reflect the latest available 
published information. Sources of dwelling trajectories vary across local authorities due to different planning 
systems and the lack of statutory requirements in regards to reporting. 
4.4.4. Dwelling growth was obtained from the housing trajectories contained within the latest Authority 
Monitoring Reports (AMR) or Strategic Household Land Availability Assessment published by each local 
authority. The publication year of these documents varies by authority but is predominantly 2013 or 2014. A 
description of data sources is included in Appendix F of this report. 
4.4.5. The trajectories provided by the authorities predominantly extend to 2026. To extend these to 2051, a 
trend-based approach was adopted assuming that average growth rates continued to the time horizon of the 
model. 
4.4.6. Table 4-6 summarises total dwelling projections by year and study area input into the NTEM forecasting 
process. 
Table 4-6: Forecast year dwellings by study area (thousands) 
Study area 
2016 
2021 
2026 
2031 
2036 
2041 
2046 
2051 
East Midlands 
2,061 
2,172 
2,266 
2,366 
2,465 
2,563 
2,662 
2,760 
North East 
1,210 
1,255 
1,298 
1,343 
1,387 
1,431 
1,475 
1,519 
North West 
3,255 
3,397 
3,508 
3,629 
3,748 
3,866 
3,984 
4,103 
Scotland 
2,593 
2,694 
2,793 
2,893 
2,992 
3,091 
3,190 
3,289 
South West 
2,515 
2,646 
2,754 
2,867 
2,979 
3,091 
3,203 
3,314 
Wales 
1,445 
1,513 
1,578 
1,644 
1,710 
1,776 
1,842 
1,908 
West Midlands 
2,456 
2,558 
2,653 
2,748 
2,842 
2,937 
3,031 
3,126 
Wider South East 
10,028 
10,556 
10,991 
11,426 
11,865 
12,303 
12,741 
13,179 
Yorkshire & the Humber 
2,413 
2,559 
2,695 
2,828 
2,963 
3,098 
3,233 
3,367 
Total 
27,977 
29,351 
30,537 
31,743 
32,950 
34,156 
35,361 
36,566 
 
4.5. 
Employment  
4.5.1. Forecast year employment data provides the number of jobs segmented by: 
 
Gender; 
 
Employment status (full time or part time); and 
 
Employment sector (for the twelve NTEM categories). 
4.5.2. Those in full time employment are defined as those that work 30 hours or more per week. Part time 
employment is anything less than this. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
37
 
Exogenous employment forecasts (long term) 
4.5.3. The latest Office for Budget Responsibility (OBR) forecasts of employment are used to provide the 
long term trend in employment. A factor is derived for each forecast year, which is then applied to the base 
data. 
Figure 4-1: National employment projections (OBR trend) 
 
4.5.4. Working Futures (WF) is a labour market model that provides detailed projections of GB jobs by 
occupation and industry. The data includes yearly employment projections up to 2022 by industry, gender and 
pre-defined age category at study area level.  
4.5.5. Results for employment projections have the following dimensions: 
 
Gender; 
 
Employment status; 
 
Occupation; 
 
Industry / sector; and 
 
Geography. 
4.5.6. At a national level, WF provides data5 for 75 industries, which are broadly consistent with SIC 2-digit 
codes. Sub-national projections (by LEP zone) are aggregated into 22 industry types. See Appendix E for 
details of the 22 WF industry types and the relevant SIC 2007 division (2-digit SIC code). 
4.5.7. As there is no direct equivalence to NTEM employment categories (the most detailed of which contain 
SIC 5-digit codes), a correspondence between the two datasets is sought. Crucially, absolute values for 
employment volumes cannot be used. Instead, a trend can be derived according to the most relevant WF 
industry types. Table 4-7 shows the source of trend for each NTEM employment type. A list of Working Futures 
industry types is included in Appendix E of this report. 
                                                     
5 Working Futures 2012-2022, Technical Report (UK Commission for Employment and Skills, Warwick Institute for Employment 
Research, Cambridge Econometrics, March 2014) 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
38
 
4.5.8. LEP zones are made up of local authority districts, consistent with control area geography in NTEM. 
Growth factors derived for LEP zones can therefore be applied to the relevant control area. In a minority of 
cases, local authority districts may belong to more than one LEP zone. To ensure that employment growth is 
not overstated, regional growth (also provided by the Working Futures dataset) is calculated and used as an 
ultimate constraint.  
Table 4-7: NTEM 7 employment types and WF industry type for trend calculation 
NTEM employment type 
Source of trend – WF (22) industry type 
E03 Primary & Secondary Schools 
19 
E04 Higher Education 
19 
E05 Adult Education 
19 
E06 Hotels, Campsites etc. 
11 
E07 Retail Trade 
9 
E08 Health, Medical 
20 
E09 Services & Eqpt Rental 
9, 10, 16, 17, 20 
E10 Industry, Construct, Transport 
2, 3, 4, 5, 6, 7, 8, 10 
E11 Restaurants & Bars 
11 
E12 Recreation & Sport 
21 
E13 Agriculture & Fishing 
1 
E14 Business 
All other codes 
4.5.9. Working Futures industry categories are not of sufficient granularity, this is done on a gender and 
working status basis. Where LEP data is not available, the aggregate regional trend is used. In order to provide 
regional totals of employment until 2051, regional proportions are applied to the OBR consistent national 
forecast. 
4.5.10. As part of the forecasting process, the employment totals (jobs) are converted to numbers of workers 
within the Scenario Generator using a parameter that defines the ratio of workers () within each employment 
sector by gender and working status to jobs for each study area. This is described in more detail in Appendix 
C. Table 4-8 shows the resulting forecast employment data for use in the NTEM dataset. 
Table 4-8: Total employment (jobs) projections by type (thousands) 
Type 
2016 
2021 
2026 
2031 
2036 
2041 
2046 
2051 
E03 
2,358 
2,308 
2,343 
2,371 
2,407 
2,444 
2,487 
2,523 
E04 
461 
442 
449 
454 
461 
468 
476 
483 
E05 
273 
263 
267 
271 
275 
279 
284 
288 
E06 
508 
526 
534 
540 
548 
557 
566 
575 
E07 
3,720 
3,862 
3,920 
3,968 
4,028 
4,089 
4,161 
4,221 
E08 
3,212 
3,388 
3,438 
3,480 
3,533 
3,586 
3,650 
3,703 
E09 
2,978 
3,070 
3,115 
3,154 
3,202 
3,250 
3,307 
3,355 
E10 
7,262 
7,449 
7,560 
7,653 
7,769 
7,886 
8,025 
8,142 
E11 
1,317 
1,383 
1,404 
1,421 
1,442 
1,464 
1,490 
1,512 
E12 
1,132 
1,084 
1,100 
1,113 
1,130 
1,147 
1,168 
1,184 
E13 
291 
300 
304 
308 
313 
317 
323 
328 
E14 
8,768 
8,876 
9,008 
9,119 
9,258 
9,396 
9,563 
9,701 
Total 
32,280 
32,951 
33,441 
33,852 
34,367 
34,882 
35,500 
36,015 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
39
 
5. 
Expected growth factors 
5.1. 
Summary 
5.1.1. Expected Growth Factors (EGFs) influence how much employment and household development from 
the control area total is distributed to its constituent NTEM zones. In practice this will be governed by a large 
array of influences, such as pulls of demand and constraints of supply. The EGF methodology is necessary in 
the absence of a complete set of specific data of individual housing and employment sites over time below the 
control area level. 
5.1.2. In order to forecast trip ends at the NTEM zone level to the horizon of the model, forecasts of 
demographic information are required at this spatial resolution. However, such forecasts are not available on 
as detailed a spatial level as the base data. Therefore the purpose of the EGFs is to distribute forecast growth 
for a control area amongst the constituent NTEM zones. 
5.1.3. There are two sets of EGFs required: 
 
Household growth for zones within a control area; and 
 
Employment growth for zones within a control area, which requires growth rates for each 
employment industry by zone. 
5.1.4. There are two different schemes for applying EGFs: growth weights and growth trends. The Scenario 
Generator tool is capable of using either one of these methods as determined by the user. 
 
Weights: EGFs are used as a basis of proportional change expected in each zone for the variable 
in question; and 
 
Trends: EGFs are used as relative linear factors that will distribute to each zone within a control 
area based on their relative sizes. This is practically the same as providing absolute expected 
growth (or decline) over time. 
5.1.5. Figure 5-1 shows the impact of the two approaches graphically. The points on the left of the charts are 
the initial values prior to the application of EGFs, whilst the points on the right of the charts are the zonal values 
post application of EGFs. In the weights based approach the adjustment is multiplicative, while in the trends 
based approach it is additive.  
Figure 5-1: Expected growth factors applied as weights and trends 
 
5.1.6. As with previous versions, the forecasts delivered for NTEM are generated using the weights-based 
methodology. 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
40
 
5.2. 
Household EGFs 
 
 
 
 
 
5.2.1. Household growth factors are required for each NTEM zone to represent the change in total 
households. These growth factors are derived by assessing historical changes between 2001 and 2011 for 
each 2011-based planning area type in order to formulate NTEM zone level household EGFs. The area types 
have been taken from ‘The 2011 Rural-Urban Classification for Small Area Geographies’ for England & Wales6, 
and ‘Urban Rural Classification’ for Scotland7. 
5.2.2. The rationale of these planning area types based on clustering together areas that possess similar 
demographic and morphological characteristics. This resulted in the identification of area types for Great Britain 
that should share similar patterns of housing development over time. Figure 5-2 shows the planning area types 
derived for each NTEM zone in 2011. 
5.2.3. EGFs for Scotland were calculated separately to those for England & Wales due to differences in 
Census geography. In addition to Census geography differences, there are also variances in area type 
definitions (Table 5-1). However as the aim of EGFs is to group together 'similar' areas and since they are 
defined regionally, it is not an issue that the definitions change slightly between study areas. 
Table 5-1: NTEM 7 EGF area type definitions 
NTEM area 
type ID 
NTEM  
area type 
England and Wales 
 area type 
Scotland  
area type 
1 
Urban major conurbation 
Urban major conurbation 
Large Urban Areas 
2 
Urban minor conurbation 
Urban minor conurbation 
Other Urban Areas 
3 
Urban city and town 
Urban city and town 
Accessible Small Towns 
4 
Urban city and town in a 
sparse setting 
Urban city and town in a 
sparse setting 
Remote Small Towns 
5 
Rural town and fringe 
Rural town and fringe 
Very Remote Small Towns 
6 
Rural town in a sparse 
setting 
Rural town and fringe in a 
sparse setting 
Accessible Rural Areas 
7 
Rural village and dispersed 
Rural village and dispersed 
Remote Rural Areas 
8 
Rural village and dispersed in 
a sparse setting 
Rural village and dispersed in 
a sparse setting 
Very Remote Rural Areas 
                                                     
6 England & Wales Planning Area Types: 
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/239478/RUC11user_guide_28_Aug.pdf 
 
7 Scotland Planning Area Types: http://www.gov.scot/Topics/Statistics/About/Methodology/UrbanRuralClassification/Urban-Rural-
Classification-2011-12/2011-2012-Urban-Rural-Lookups 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
41
 
Figure 5-2: 2011 planning area types used in household EGF calculation 
 
 
N.B. All Scottish islands are included, but may appear off the map. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
42
 
5.2.4. The trend in household change from 2001 to 2011 was taken from Census household data. Due to 
significant differences in the geography of the two sources, a method was used to allow a comparison between 
them. Separate methods have been used for England & Wales and Scotland. 
5.2.5. As a result of MSOA boundary changes between census years in England & Wales, EGFs have only 
been calculated for MSOAs that remain unchanged between 2001 and 2011 Censuses. This approach 
assumes the planning area type of unchanged MSOAs are identical. Of the 7,201 2011 MSOAs, 7,040 have 
remained unchanged.  
5.2.6. Census tables 'UV051' (Number of people living in households) from the 2001 Census and 'QS406EW' 
(Household size) from the 2011 Census have been utilised to calculate an EGF for all unchanged MSOAs in 
England & Wales. The total number of households within a study area was calculated by identifying the number 
of households within all unchanged MSOAs of a given study area. Subsequently an EGF was produced for 
each planning area type within each study area of England & Wales based on 2001 and 2011 household 
Census data. 
5.2.7. For Scotland, each DZ from the 2001 and 2011 Census has been assigned an NTEM zone, as a lookup 
between 2001 and 2011 DZs does not exist. It has thus been assumed the planning area type of an NTEM 
zone has remained the same between Census years (2001 and 2011). 
5.2.8. Census tables 'UV51' (Number of people living in households) from the 2001 census and 'QS406SC' 
(Household size) from the 2011 census have been utilised to calculate an EGF for all Scottish NTEM zones. 
The total number of households within Scotland was calculated by identifying the number of households within 
each NTEM zone. Subsequently an EGF was produced for each planning area type within Scotland based on 
2001 and 2011 household census data. 
5.2.9. To increase the accuracy of the Planning Area Type methodology, EGFs are defined separately 
according to study area. This contributes towards accounting for any regional variation in planning policy that 
may exist.  
5.2.10. Table 5-2 shows the assumed household EGFs by planning area type in each study area between 
2001 and 2011. Note that the values in Table 5-2 are rounded to the nearest whole percentage. 
 
Table 5-2: Household growth factors 
Area  
type 
Study area 
East  
Midlands 
North  
East 
North  
West 
Scotland 
Wider 
South 
East 
South 
West 
Wales 
West 
Midlands 
Yorks & 
Humber 
1 
6% 
5% 
6% 
7% 
8% 
- 
- 
4% 
8% 
2 
6% 
- 
- 
9% 
- 
- 
- 
- 
6% 
3 
10% 
7% 
6% 
12% 
8% 
8% 
8% 
8% 
8% 
4 
12% 
6% 
8% 
16% 
- 
5% 
6% 
14% 
2% 
5 
11% 
8% 
6% 
10% 
8% 
8% 
7% 
8% 
10% 
6 
16% 
8% 
2% 
6% 
2% 
15% 
7% 
- 
9% 
7 
9% 
15% 
8% 
7% 
8% 
6% 
9% 
8% 
9% 
8 
6% 
8% 
9% 
7% 
-1% 
7% 
8% 
7% 
7% 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
43
 
5.2.11. NTEM zones of the same planning area type within a study area are assumed to have the same level 
of growth. The final household EGFs for each study area are therefore a weighted average of growth according 
to the Planning Area Type of the MSOAs or DZs that nest within them. 
5.2.12. On allocating households at the zone level, these EGFs are used to growth up the households that 
already exist in those zones. Total growth is controlled to the total number of households expected in the 
control area. Therefore, as described in section 5.1, EGFs are adjusted either proportionally (weights-based 
approach) or linearly (trend-based approach) in order to obtain the required district household total (see 
example below). In NTEM EGFs are adjusted using the weights-based methodology. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXAMPLE 
 
Average Expected Growth Rate (EGF) 
 
Zone 1: 9% 
Zone 2: 12% 
 
Zone 2 would be expected to receive more household growth than Zone 1 
relative to its initial stock. This of course is not necessarily a greater total 
number of households as this will depend on relative initial size and the total 
internal adjustment required to the growth factors. 
 
In 2011 Zone 1 has 4,979 households and Zone 2 has 1,779. Using the growth 
factors, the initial estimate of households is thus: 
 
Zone 1 4,979 * 0.09 = 448 households 
Zone 2 1,779 * 0.12 = 213 households 
 
As an example, if only these two zones were in the control area and the 
household growth total is 800, the growth rates need to be factored up to 
achieve this level of growth. 
 
Using a weight-based approach this is proportionately (maintaining the same 
ratio between growth rates by multiplying through to get the correct growth 
required): 
 
Zone 1 4,979 * (0.09 * 800/661) = 4,979 * 0.109     = 542 households 
Zone 2 1,779 * (0.12 * 800/661) = 1,779 * 0.145     = 258 households 
 
Using a trend-based approach this is linearly (maintaining the same absolute 
difference between growth rates): 
 
An extra 139 households are required in addition to the already established 
growth 
 
4,979 + 1,779 = 6,758 
(800 - 448+213) / 6,758 = 139 / 6,758 = 0.0205  
 
Zone 1 4,979 * (0.09 + 0.0205) = 4,979 * 0.1105     = 550 households 
Zone 2 1,779 * (0.12 + 0.0205) = 1,779 * 0.1405     = 250 households 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
44
 
5.3. 
Employment EGFs 
5.3.1. Employment types that are Dwellings-led do not require input growth factors as they will be allocated 
an EGF using generated household results in the Scenario Generator tool. 
5.3.2. EGFs in NTEM are produced using a weights-based approach. These factors are set as constant, 
therefore causing growth to be distributed pro rata according to existing distribution. 
5.3.3. Table 5-3 shows the source of EGFs for each NTEM employment type. These are derived by one of 
two approaches: 
 Dwellings-led (i.e. schools and leisure activities); or 
 Constant, all assumed to grow relative to their existing size. 
 
Table 5-3: EGF source by NTEM employment type 
Code 
NTEM employment type 
EGF source 
E03 
Primary and secondary school employment 
Dwellings-led 
E04 
Higher education employment 
Constant, relative to size 
E05 
Adult education employment 
Constant, relative to size 
E06 
Hotels, campsites, etc. 
Constant, relative to size 
E07 
Retail trade 
Constant, relative to size 
E08 
Health/ medical employment 
Constant, relative to size 
E09 
Services (business & other, eqpt rental, repairs) 
Constant, relative to size 
E10 
Industry, construction and transport 
Constant, relative to size 
E11 
Restaurants and bars 
Constant, relative to size 
E12 
Recreation and sport 
Dwellings-led 
E13 
Agriculture and fishing 
Constant, relative to size 
E14 
Business (other) 
Constant, relative to size 
 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
45
 
6. 
Planning data results 
6.1. 
Introduction 
6.1.1. This chapter outlines the results from NTEM 7 and provides a comparison between NTEM 6.2 and 
NTEM 7. This will help to explain:  
 
The main trends in the NTEM 7 dataset as output from ScenGen, National Car Ownership Model 
and National Trip End Model; and 
 
The similarities and differences between NTEM 6.2 and NTEM 7 datasets to show how the 
planning data and ultimately the trip ends may differ between the two versions.  
6.1.2. Where data has been analysed at a spatially detailed level, the forecast year of 2031 has been used 
to review the results midway through the forecasting horizon. As the base year and forecast years, geography 
and input data of NTEM 6.2 and NTEM 7 differ, a direct comparison is not necessarily appropriate. 
6.2. 
Planning data (Scenario Generator) 
6.2.1. The following demographic forecasts are compared and contrasted between NTEM version 6.2 
(referred to as v6.2) and NTEM version 7 (referred to as v7): 
 
Planning data (population, employment, households and dwellings); 
 
Average household sizes; 
 
Vacancy rates; and 
 
Housing pressure. 
Comparison of planning data outputs 
6.2.2. A high level comparison has been drawn between v6.2 and v7 forecast year planning data (population, 
employment, household and dwelling) at a global level (Figure 6-1). In general, exogenous forecasts of 
population, households and dwellings are lower in v7 when compared to v6.2, whilst employment forecasts 
are slightly higher. Each of these categories will be considered in greater detail. 
Figure 6-1: Comparison of planning data totals 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
46
 
Population 
6.2.3. Table 6-1 and Figure 6-2 compare population totals in Great Britain between v6.2 and v7. Total 
population is slightly higher in v7 than v6.2 for all forecast years, with the trends converging over time. 
6.2.4. In both cases a “central” forecast of population growth is concerned. Particular variants (high migration, 
low birth rate etc.) will of course impact this comparison. 
Table 6-1: Comparison of GB forecast population data (thousands) 
Population 
2016 
2021 
2026 
2031 
2036 
2041 
NTEM 7  
62,446 
64,523 
66,431 
68,096 
69,552 
70,984 
NTEM 6.2 
61,683 
63,746 
65,684 
67,392 
68,894 
70,337 
Difference (v7 - v6.2) 
763 
777 
747 
704 
658 
647 
Difference (%) 
1.24% 
1.22% 
1.14% 
1.04% 
0.96% 
0.92% 
6.2.5. Spatial variability is similar between the two versions. In general the growth rate in v7 between 2011 
and 2031 is lower in the South West and Yorkshire and the Humber, and slightly higher in Scotland and the 
Wider South East. 
Figure 6-2: Population growth (2011-2031) in NTEM 6.2 (left) and NTEM 7 (right) 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
47
 
Employment (jobs and workers) 
6.2.6. Table 6-2 compares job totals in Great Britain between v6.2 and v7, whilst Table 6-3 compares worker 
totals. Workers refers to the total number of people in employment.  
6.2.7. There are nearly always a greater number of jobs than there are workers due to people having multiple 
jobs. Employment totals are higher in v7 than v6.2 for all forecast years, although the difference between the 
two versions is less apparent in later forecast years.  
Table 6-2: Comparison of GB forecast employment (jobs) data (thousands) 
Jobs 
2016 
2021 
2026 
2031 
2036 
2041 
NTEM 7  
32,279 
32,951 
33,441 
33,852 
34,367 
34,882 
NTEM 6.2 
31,021 
31,576 
32,191 
32,591 
33,232 
33,911 
Difference (v7 - v6.2) 
1,258 
1,375 
1,250 
1,261 
1,135 
971 
Difference (%) 
3.90% 
4.17% 
3.74% 
3.73% 
3.30% 
2.78% 
Table 6-3: Comparison of GB forecast employment (workers) data (thousands) 
Workers 
2016 
2021 
2026 
2031 
2036 
2041 
NTEM 7  
29,523 
30,174 
30,640 
31,009 
31,448 
31,923 
NTEM 6.2 
28,327 
28,787 
29,281 
29,554 
30,063 
30,655 
Difference (v7 - v6.2) 
1,196 
1,387 
1,359 
1,455 
1,385 
1,268 
Difference (%) 
4.05% 
4.60% 
4.43% 
4.69% 
4.41% 
3.97% 
 
6.2.8. Table 6-4 compares the proportion of total jobs across each study area between v6.2 and v7. Similar 
trends exist between the two versions, with no notable differences. 
Table 6-4: Proportion of total jobs by study area 
Study area 
NTEM 6.2 
NTEM 7 
Difference (%) 
2021 
2031 
2041 
2021 
2031 
2041 
2021 
2031 
2041 
East Midlands 
7.0% 
6.9% 
6.8% 
7.1% 
7.1% 
7.1% 
0.1% 
0.2% 
0.3% 
North East 
3.7% 
3.6% 
3.6% 
3.7% 
3.7% 
3.7% 
0.0% 
0.1% 
0.1% 
North West 
10.9% 
10.7% 
10.5% 
10.7% 
10.7% 
10.7% 
-0.2% 
0.0% 
0.2% 
Scotland 
8.3% 
8.4% 
8.6% 
8.4% 
8.4% 
8.4% 
0.1% 
0.0% 
-0.2% 
South West 
8.7% 
8.6% 
8.5% 
8.9% 
8.9% 
8.9% 
0.2% 
0.3% 
0.4% 
Wales 
4.2% 
4.5% 
4.7% 
4.4% 
4.4% 
4.4% 
0.1% 
-0.1% 
-0.4% 
West Midlands 
8.4% 
8.5% 
8.6% 
8.3% 
8.3% 
8.3% 
-0.1% 
-0.2% 
-0.3% 
Wider South East 
40.5% 
40.3% 
40.2% 
40.3% 
40.3% 
40.3% 
-0.2% 
0.0% 
0.1% 
Yorks & Humber 
8.3% 
8.4% 
8.5% 
8.2% 
8.2% 
8.2% 
-0.1% 
-0.2% 
-0.4% 
Total 
100% 
100% 
100% 
100% 
100% 
100% 
-0.1% 
0.1% 
-0.2% 
 
6.2.9. In general, v7 demonstrates higher employment growth rates except in a few concentrated areas in 
Scotland, South Wales and Yorkshire and the Humber (as shown in Figure 6-3). 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
48
 
6.2.10. Considering the pattern of growth in workers, Figure 6-3 shows growth in workers between 2011 and 
2031 for NTEM 6.2 and NTEM 78. In addition to different patterns of relative growth, particularly evident is the 
impact of the increased granularity of the zoning system. 
  
Figure 6-3: Employment growth (workers, 2011-2031) in NTEM 6.2 (L) and NTEM 7 (R) 
 
 
Households 
6.2.11. The total number of households is slightly lower in v7 than v 7.2 in all forecast years. This is shown in 
Table 6-5 and Figure 6-4. The difference between the two versions varies over time, peaking in forecast year 
2036.   
 
 
                                                     
8 It should be noted that 2011 is the base year of NTEM 7, but a forecast year of NTEM 6.2 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
49
 
Table 6-5: Comparison of GB forecast year household data (thousands) 
Households 
2016 
2021 
2026 
2031 
2036 
2041 
NTEM 7  
26,948 
28,185 
29,268 
30,302 
31,262 
32,341 
NTEM 6.2 
27,529 
28,923 
30,255 
31,491 
32,631 
33,511 
Difference (v7 - v6.2) 
-581 
-738 
-987 
-1,189 
-1,369 
-1,170 
Difference (%) 
-2.16% 
-2.62% 
-3.37% 
-3.92% 
-4.38% 
-3.62% 
 
Figure 6-4: Households growth (2011-2031) in NTEM 6.2 (L) and NTEM 7 (R) 
 
 
Dwellings 
6.2.12. Table 6-6 and Figure 6-5 compare dwelling totals in Great Britain between v6.2 and v7.  
6.2.13. The total number of dwellings in v7 and v6.2 are similar for all forecast years. Dwelling totals are slightly 
lower in v7 than v6.2 across all forecast years.  
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
50
 
Table 6-6: Comparison of GB forecast year dwellings data (thousands) 
Dwellings 
2016 
2021 
2026 
2031 
2036 
2041 
NTEM 7  
27,977 
29,351 
30,537 
31,743 
32,950 
34,156 
NTEM 6.2 
28,350 
29,628 
30,789 
32,003 
33,228 
34,456 
Difference (v7 - v6.2) 
-373 
-277 
-253 
-260 
-278 
-300 
Difference (%) 
-1.33% 
-0.94% 
-0.83% 
-0.82% 
-0.84% 
-0.88% 
 
Figure 6-5: Comparison of GB forecast dwellings data  
 
 
Average household size 
6.2.14. There is a reduction in the average household size over time (Figure 6-6), which is comparable to the 
trends observed in v6.29. 
6.2.15. The Wider South East study area (London, East of England and South East) has the highest average 
household size in all forecast years as insufficient dwellings allocations prevent household formation and 
induce more people to live together in 2+ person households. Scotland has the lowest average household size 
in all forecast years. 
                                                     
9 NTEM 6.2 Planning Guidance – Figure 6.1 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
51
 
Figure 6-6: Average household size by study area 
 
6.2.16. Figure 6-7 shows average household sizes at control area level in the 2031 forecast year. The Wider 
South East study area shows the largest spread of average household sizes in v7; from 3.18 persons per 
household in Hillingdon to 1.56 in the City of London. This trend is similar to v6.2, where the Wider South East 
study area also demonstrates the largest dispersion of average household sizes10.  
Figure 6-7: Average household size by control area – forecast year 2031 
 
                                                     
10 NTEM 6.2 Planning Guidance – Figure 6.2 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
52
 
Vacancy rates 
6.2.17. Figure 6-8 shows vacancy rates by control area in 2031. The dispersion of control area vacancy rates 
in v7 is relatively similar between all study area, with the exception of the Wider South East 
6.2.18. Control area vacancy rates in the Wider South East are consistently lower than other study area due 
to continuing pressure on dwelling stock. This is consistent with that seen in NTEM v6.2. 
6.2.19. Vacancy rates are generally higher than those shown in NTEM v6.211. This is sensible given the slightly 
lower projections of households and similar projections of dwellings used in NTEM v7. 
Figure 6-8: Vacancy rates by control area – forecast year 2031 
 
Housing pressure  
6.2.20. Where household capacity is insufficient within a control area, 50% of the excess households are 
reallocated evenly to other control areas with vacant dwellings within the study area, whilst the other 50% of 
excess households are suppressed.   
6.2.21. Figure 6-9 shows household capacity before reallocating or suppressing excess households in v7. The 
overall trend in v7 indicates that housing capacity increases over time, with the exception of the Wider South 
East study area. This is similar to the trend observed in NTEM v6.2 
6.2.22. The overall trend in v6.2 also implies that housing capacity generally increases over time12. Despite 
trend similarities between the two versions, average household capacities are greater in v7 than v6.2. 
However, this capacity is more evenly distributed in NTEM v7, with the highest observed household capacity 
much greater in v6.2. 
6.2.23. North West and Scotland stand out as study areas with a significantly greater level of household 
capacity in the mid to later forecast years, whereas Yorkshire and the Humber has the highest household 
capacity in v7. Differences between study area household capacities are not as great in v7 compared to v6.2.  
                                                     
11 NTEM 6.2 Planning Guidance – Figure 6.3 
12 NTEM 6.2 Planning Guidance – Figure 6.4 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
53
 
Figure 6-9: Household capacity before reallocating or suppressing excess households 
 
6.2.24. It is often the case during the reallocation process that housing demand within a control area cannot 
be accommodated by the supply of dwellings. As explained in Section 2.4 (‘combining trend and policy based 
estimates’), this may result in excess households within a control area being supressed. Figure 6-10 and Figure 
6-11 show the number of households suppressed during the reallocation process in NTEM v7. 
 
Figure 6-10: Households suppressed by study area (excluding Wider South East) 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
54
 
Figure 6-11: Households suppressed by study area (Wider South East) 
 
6.2.25. The suppression of households in ScenGen increases over time, and is observed to much greater in 
the Wider South East study area.  
6.2.26. The overall trend is very similar to that observed in NTEM v6.213. However in general, household 
suppression is greater in v6.2 than v7. This is consistent with the fact that household projections are greater 
in v6.2 than v7.  
6.3. 
National Car Ownership model (NATCOP) 
6.3.1. Outputs from the Scenario Generator are used in the National Car Ownership Model. This calculates 
the probability of households owning 0, 1, 2 and 3 or more cars in a given forecast year. In addition to 
demographic information provided by the Scenario Generator, NATCOP uses household income at the zone 
level14 and WebTAG derived trends for vehicle running costs.    
6.3.2. The National Car Ownership model has been re-estimated and updated to a 2011 base year, including 
additional area types (with a better representation of London) and a cohort licence holding model to better 
reflect changes in driving licence holding over time. 
6.3.3. Detailed information on NATCOP is contained within the report, “Estimation of the National Car 
Ownership Model for Great Britain, 2011 Base”15. 
6.3.4. Car ownership forecasts between NTEM 6.2 (v6.2) and NTEM 7 (v7) are compared in Table 6-7. There 
is a higher proportion of zero car households in v7, whilst there is a smaller proportion of one car households. 
In regards to 2+ car households there is minimal difference between the two versions, although slightly higher 
in v7. There is a greater proportion of 3+ car households in v7, especially in the later forecast years. The total 
number of cars in 2011 is lower in v7, but by 2041 it is greater in v7. This may be accredited to a rise in multi-
car owning households. 
                                                     
13 NTEM 6.2 Planning Guidance – Figure 6.5 
14 Provided by CACI, © CACI Limited Paycheck 1996-2016 
15 RAND Europe, 2016 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
55
 
6.3.5. The difference between car ownership proportions is relatively consistent across forecast years, with 
the greatest difference across all car ownership proportions occurring in 2041, the latest year for which both 
forecasts are available. 
 
Table 6-7: Forecast car ownership comparison  
Year 
Household Car Ownership 
(thousands) 
Total 
Cars 
Proportion (%) 
0 cars 
1 car 
2 cars 
3+ cars 
P0 
P1 
P2 
P3P 
  
Version 6.2 
2011 
5,908 
12,319 
6,298 
1,582 
29,977 
23% 
47% 
24% 
6% 
2016 
5,713 
13,207 
6,806 
1,727 
32,345 
21% 
48% 
25% 
6% 
2021 
5,860 
13,917 
7,149 
1,817 
34,029 
20% 
48% 
25% 
6% 
2026 
5,940 
14,512 
7,486 
1,950 
35,724 
20% 
49% 
25% 
7% 
2031 
6,016 
15,133 
7,802 
2,064 
37,342 
19% 
49% 
25% 
7% 
2036 
6,036 
15,705 
8,129 
2,209 
39,032 
19% 
49% 
25% 
7% 
2041 
6,010 
16,157 
8,453 
2,388 
40,705 
18% 
49% 
26% 
7% 
 
Version 7 
2011 
6,741 
10,877 
6,295 
1,871 
29,454 
26% 
42% 
24% 
7% 
2016 
6,736 
11,399 
6,544 
2,270 
31,750 
25% 
42% 
24% 
8% 
2021 
6,735 
12,005 
7,022 
2,429 
33,821 
24% 
43% 
25% 
9% 
2026 
6,683 
12,500 
7,500 
2,588 
35,782 
23% 
43% 
26% 
9% 
2031 
6,630 
12,939 
7,940 
2,794 
37,759 
22% 
43% 
26% 
9% 
2036 
6,507 
13,320 
8,456 
2,982 
39,774 
21% 
43% 
27% 
10% 
2041 
6,422 
13,733 
9,038 
3,152 
41,894 
20% 
42% 
28% 
10% 
 
Difference (v7 - v6.2) 
2011 
14% 
-12% 
0% 
18% 
-2% 
4% 
-5% 
0% 
1% 
2016 
18% 
-14% 
-4% 
31% 
-2% 
4% 
-6% 
-1% 
2% 
2021 
15% 
-14% 
-2% 
34% 
-1% 
4% 
-6% 
0% 
2% 
2026 
13% 
-14% 
0% 
33% 
0% 
3% 
-6% 
1% 
2% 
2031 
10% 
-14% 
2% 
35% 
1% 
2% 
-6% 
1% 
3% 
2036 
8% 
-15% 
4% 
35% 
2% 
2% 
-6% 
2% 
3% 
2041 
7% 
-15% 
7% 
32% 
3% 
2% 
-6% 
2% 
3% 
The total car calculation is illustrative and assumes 3+ car households have an average of 3.2 vehicles 
6.3.6. Figure 6-12 shows the average number of cars per household in v6.2 and v7 by NTEM zone in 2031. 
Dense urban areas tend to have fewer cars per household than zones outside of metropolitan areas. The 
Wider South East, outside of London, has the highest level of car ownership in v7. Trends are comparable 
between v6.2 and v7, although in general the average number of cars per household is slightly higher in v7. 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
56
 
Figure 6-12: Car ownership per household (2031) in NTEM 6.2 (L) and NTEM 7 (R) 
 
 
6.3.7. Figure 6-13 and Figure 6-14 show growth16 in households by car ownership between 2011 and 2031, 
in v6.2 and v7. This demonstrates the general trend of increased car ownership through time across Great 
Britain, with greater levels of growth in higher car owning households.  
6.3.8. For the majority of Great Britain there is a reduction in zero car households over time, however in 
densely populated areas there is an increase between 2011 and 2031 in v6.2 and v7. Growth in one and two 
car households are relatively similar in both versions. The growth in households with three or more vehicles is 
greater in v7 than v6.2, across Great Britain. This should be considered in the context of Table 6-7 (i.e. there 
is a lower overall number of 3+ car households, and so growth is from a smaller base). 
 
                                                     
16 i.e. 1.5 indicates 50% growth 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
57
 
Figure 6-13: Growth in households by car ownership (2011-2031) in NTEM 6.2 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
58
 
Figure 6-14: Growth in households by car ownership (2011-2031) in NTEM 7 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
59
 
6.4. 
National Trip End Model (CTripEnd) 
6.4.1. The National Trip End Model gives details of trip productions and attractions that are ultimately made 
available through the TEMPRO system. Whilst the software has been updated (as mentioned in Section 1.3), 
there has been no change to the underlying methodology. 
6.4.2. As part of the Department for Transport’s, “Understanding and valuing impacts of transport investment” 
(UVITI) research programme, home-based trip rates have been re-estimated17.These trip rates apply to 8 
home-based purposes across 88 traveller types (incorporating age, gender, working status and car availability) 
and 8 different area types.  
6.4.3. Recent trends18 in personal travel have demonstrated a decline in trip making over time, with reductions 
since the late 1990s. Research carried out on an extended National Travel Survey (NTS) sample has 
confirmed this for the dimensions required by NTEM. Whilst a full analysis of trip rates is outside the scope of 
this report, the implementation of the updated evidence base on home-based trip making has reduced total 
trip productions in 2011 by around 20% when comparing with NTEM 6.2. 
6.4.4. Trip rates were estimated from observed data for the base year of 2011. A forecasting model (the Trip 
Rate Tool, as mentioned in Section 1.3) was used to estimate trip rates in 2016, after which point they are held 
constant. This results in lower trip rates for 2011 and subsequent forecast years when compared with NTEM 
6.2. 
Comparison of total trips and growth through time 
6.4.5. The volume of trips (shown here as total trip productions) estimated in NTEM v7 is substantially lower 
than that estimated in NTEM 6.219 (Table 6-8 and Figure 6-15). The impact of changing trip rates through time 
is apparent, with lower trip totals estimated in the forecast year 2016 when compared to the 2011 base. 
6.4.6. As the model is conducted at 5-year increments, intervening years are calculated through linear 
interpolation and made available in TEMPRO. 
Table 6-8: Total trip productions in GB comparison (millions per average day) 
Version 
2011 
2016 
2021 
2026 
2031 
2036 
2041 
2046 
2051 
NTEM 6.2 
100.5 
104.2 
107.5 
110.7 
113.4 
115.9 
118.7 
- 
- 
NTEM 7 
81.2 
79.4 
82.1 
84.4 
86.4 
88.2 
90.2 
92.2 
94.1 
Difference 
-19.3 
-24.8 
-25.4 
-26.3 
-27.0 
-27.7 
-28.5 
- 
- 
Difference (%) 
-19% 
-24% 
-24% 
-24% 
-24% 
-24% 
-24% 
- 
- 
 
6.4.7. Sensitivity analyses have demonstrated that the major driver behind this are the reduced trip rates 
estimated from NTS.  Application of previously used trip rates to the version 7 dataset produces slightly higher 
forecasts of trips, reflecting the marginally higher forecasts of population and employment (discussed in 
Section 6.2). 
                                                     
17 This work is due to be published in Autumn 2016 
18 For example, from the National Travel Survey (Department for Transport, 2015) 
19 Whilst the base year of NTEM 6.2 was 2001, only results from 2011 onwards will be shown here 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
60
 
Figure 6-15: Change in trip productions over time - NTEM 6.2 and NTEM 7 
 
6.4.8. Updates to trip rates in line with recent NTS data means that direct comparisons between NTEM 6.2 
and NTEM 7 are difficult. To aid this, Figure 6-16 shows the difference in growth rate in total trip productions 
for Great Britain, normalised so 2011 is equal to 100 in both datasets. The effect of the trend in trip rates 
(discussed in Section 6.4.4) is apparent with a decrease in total trip productions between 2011 and 2016. 
Growth rates for total trip productions from 2016 onwards are broadly similar between the two datasets, as 
illustrated in Figure 6-16. 
 
 
Figure 6-16: Growth in trip productions from 2011 – NTEM 6.2 and NTEM 7 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
61
 
Trips by purpose 
6.4.9. 
NTEM includes 15 trip purposes (8 home based, 7 non-home based). Figure 6-17 shows total trip 
productions for an average day between 2011 and 2051, split by trip purpose.  
 
Figure 6-17: Total trip productions by purpose in Great Britain – NTEM 7 
 
 
6.4.10. Figure 6-18 and Figure 6-19 compare the proportion of total trip productions by purpose between v6.2 
and v7, in 2011 and 2031 respectively. Split by purpose is relatively similar between versions, and between 
these two model years.  
6.4.11. The largest difference between v6.2 and v7 is associated with home-based recreation trips. In 2011 
and 2031, home based recreation trips account for 4% less total trip productions in v7 compared to v6.2. 
6.4.12. The largest proportional increase between v6.2 and v7 is associated with home based education trips. 
In 2011 home based education trips account for an extra 3% of total trip productions in v7 compared to v6.2, 
and an extra 2% in 2031. This may be attributed to an increase in the total number of people in education. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
62
 
 
Figure 6-18:  Proportion of total trip productions by purpose (2011) - NTEM 6.2 and NTEM 7 
 
 
Figure 6-19:  Proportion of total trip productions by purpose (2031) - NTEM 6.2 and NTEM 7 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
63
 
Average trips per household 
6.4.13. Figure 6-20 and Figure 6-21 show the average number of daily trip productions per household between 
2011 and 2041, for v6.2 and v7 respectively. Both versions show a similar trend of decline in trip productions 
per household over time, which begins to level out in the later forecast years. As trip rates are applied to 
individuals rather than households, this is consistent with earlier results (Section 6.2) of declining household 
size over time.  
 
Figure 6-20:  Average daily trip productions per household by study area - NTEM 6.2 
 
 
Figure 6-21:  Average daily trip productions per household by study area - NTEM 7 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
64
 
Average trips per person 
6.4.14. Figure 6-22 shows the average number of daily trip productions per person between 2011 and 2051, 
in v7. There is a decline in the number of trips between 2011 and 2016, representative of the trip rates used 
in v7 (as discussed earlier in this section). Change to modelled trip productions post 2016 (which is negligible) 
is a result of changing population mix and car ownership characteristics. 
 
Figure 6-22: Average daily trip productions per person by study area - NTEM 7 
 
 
Geographical distribution of trips 
6.4.15. Figure 6-23 compares growth in the total number of trip productions and attractions between 2011 and 
2031, for v6.2 and v7. As discussed earlier in this section, growth from 2011 is lower in v7 due to an initial 
reduction in trip rates. 
6.4.16. The distribution of growth is similar across the two datasets, concentrated in the East, South East and 
major metropolitan areas. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
65
 
Figure 6-23: Growth in trip productions and attractions (2011-2031) in NTEM 6.2 (L) and NTEM 7 (R) 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
66
 
Modal split 
6.4.17. The ultimate application of the NTEM dataset, through the TEMPRO software, also includes 
productions my mode and time of day. As with trip rates, parameters to determine these splits were re-
estimated from an updated National Travel Survey (NTS) sample. 
6.4.18. In order to provide a comparison, Figure 6-24 shows growth in modal trip productions between 2011 
and 2041, for v6.2 and v7 respectively. Differences in growth are due to the different population characteristics, 
including car ownership. Whilst growth is lower in general (as expected based on results presented earlier in 
this section), similar trends are apparent. Highest growth rates are present in private, motorised modes, with 
lower growth in public transport modes (rail, bus). 
6.4.19. It should be noted that modal split in NTEM is constant through time and ignores changes in the cost 
of motoring (fuel and taxation) and public transport fares as well as other components of the generalised cost 
for travel. Modal growth may therefore form a starting point for appropriate modelling of mode choice where 
generalised cost needs to be taken into account. They should therefore be treated with caution and the users 
need to satisfy themselves that they are fit for purpose. 
 
Figure 6-24: Growth rate of modal trip productions from 2011 – NTEM 6.2 and NTEM 7 
 
 
 
 

 
Appendices 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
68
 
Appendix A. Geography 
NTEM zone system 
The different levels of geography are, from smallest to largest, NTEM zones, control areas and study areas. 
All levels are geography are compatible, and so: 
NTEM zones ϵ control areas ϵ study areas 
NTEM 6.2 had a zone structure formulated from 2001 Census definitions of Output Areas, aggregated in such 
a way that they represent different area types (level and scale of urbanisation) and nest within local authority 
districts. This resulted in 2,496 NTEM zones. 
The major source of data for the NTEM dataset is the Census of Population. As NTEM 7 is based on the 2011 
Census of Population, it was necessary to reformulate the zone system to be compatible with changing Census 
geographies. 
NTEM 7 makes use of Middle Super Layer Output Areas as the lowest level of geography in England and 
Wales. This is consistent with the 2011 Census of Population, and the majority of input data, minimising the 
treatment and manipulation required. Control area and study area definitions remain the same, but reflect 
changing administrative boundaries. 
As such, the zone system is therefore not directly compatible with previous versions of NTEM, as illustrated in 
Figure A-1. 
Figure A-1: NTEM geography comparison 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
69
 
The Scottish Census adopts different geographies, with no direct equivalence (in terms of population and 
number of households) being available. Therefore a bespoke zoning system was developed, with the 
following main criteria: 
 
The zone system must be compatible with other levels of NTEM geography (i.e. control areas); 
 
The zone system must be granular enough for model processes (e.g. representation of 
urban/rural split);  
 
The zone system must be suitable for acquiring the correct data inputs; and 
 
The zone system must be granular enough for the purposes of model outputs. 
The resulting zone system matches these criteria. A comparison of the NTEM 6.2 and NTEM 7 zone system 
is shown for each TEMPRO study area in Table A-1. 
Table A-1: NTEM zone comparison 
Study area 
Number of NTEM 
zones (v6.2) 
Number of NTEM 
zones (v7) 
Growth (v7/v6.2) 
East of England 
295 
736 
2.49 
East Midlands 
260 
573 
2.20 
London 
57 
983 
17.25 
North East 
132 
340 
2.58 
North West 
282 
924 
3.28 
Scotland 
264 
499 
1.89 
South East 
410 
1,108 
2.70 
South West 
244 
700 
2.87 
Wales 
179 
410 
2.29 
West Midlands 
168 
735 
4.38 
Yorkshire and Humber 
205 
692 
3.38 
Great Britain 
2,496 
7,700 
3.08 
 
Control area geography is again based on local authority districts in England and Wales and Scottish council 
areas, reflecting updated administrative boundaries, with a reduction to 380 control areas in total. Study areas 
have also been retained, representing the English regions, Scotland and Wales. Figure A-2 shows the NTEM 
7 zone system. 
Balancing areas have also been reformulated based on the 2011 Census20. Using the existing definition of 
balancing areas as a starting point, these were revised to be compatible with the 2011 Travel to Work Areas 
(TTWA) and new NTEM zoning system. The resulting balancing areas21 are shown in Figure A-3. 
 
 
                                                     
20 Census table WU02 
21 Whilst this is not a requirement, these balancing areas are identical to those implemented in the trip end 
model, CTripEnd 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
70
 
Figure A-2: NTEM 7 zoning system 
 
N.B. All Scottish islands are also included but may appear off the map. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
71
 
Figure A-3: NTEM 7 balancing areas 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
72
 
Appendix B. Segmentation definitions 
Population segments 
Population is segmented by: 
 
Age / life stage (input); 
 
Age / life stage (forecasting and output); 
 
Gender; and 
 
Working status (for the 16-74 age groups only). 
Table B-1: Population segmentation 
Age / life stage 
(input) 
Age / life stage  
(forecasting segment) 
Gender 
Working status  
(16-74 age groups only) 
0 – 15 
16 – 29 
30 – 44 
45 – 64 
65 – 74 
75 – 79 
80 – 84 
85+ 
0 – 15 
16 – 29 
30 – 44 
45 – 64 
65 – 74 
75+ 
Male 
Female 
Full time (≥ 30 hours per week) 
Part time (< 30 hours per week) 
Students (not working) 
Other non-working 
 
Household segments 
Households are segmented by number of persons (not just adults). There are two segments: 
 
1 person households; and 
 
2+ person households. 
 
Employment segments 
Employment (jobs) is segmented by: 
 
Sector (based on SIC 2007 definitions); 
 
Gender; and 
 
Working status. 
There are 12 employment sectors defined in the planning data forecasts. These relate to the trip attraction 
variables required by NTEM. The definitions are as shown below. The gender and working status 
segmentation is as defined above for the population. 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
73
 
Table B-2: Employment segmentation 
NTEM employment type 
SIC (2007) code 
Description 
E03 
851, 852, 853 
Pre-primary, primary and secondary 
education 
E04 
854 
Higher education 
E05 
855, 856 
Adult and other education 
E06 
55 
Accommodation (hotels and 
campsites) 
E07 
451, 453, 471 – 478, 56103 
Retail trade 
E08 
86, 87 
Human health and residential care 
activities 
E09 
452, 454, 53, 6419, 683, 771, 772, 
75, 88 
Services (rental, repair, postal 
activities, real estate and other) 
E10 
05 – 09, 10 – 33, 35 – 39, 41 – 43, 
46, 49 – 52 
Industry, construction and transport 
E11 
56101, 56102, 563 
Restaurants and bars 
E12 
5914, 79901, 90 – 94 
Recreation and sport 
E13 
01, 02, 05 
Agriculture, fishing, forestry and 
logging 
E14 
All other codes 
Other employment 
 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
74
 
Appendix C. Demographic parameters 
Summary 
There are several sets of assumptions that are required in the operation of the Scenario Generator in addition 
to the base and forecast year inputs. These affect the way in which population, households and employment 
are allocated to zones in forecast years. 
The following parameters are required by the Scenario Generator: 
 
Expected number of people in each household type (by type, gender and life stage in each control 
area); 
 
Maximum acceptable difference between households and dwellings; 
 
Minimum number of average adults per household; 
 
Maximum number of average adults per household; 
 
Occupancy rate (number of households that occupy household spaces); 
 
Proportion of population in communal establishments (by age and gender at the study area level); 
 
Dwelling vacancy rate; 
 
Number of dwellings not used as main residence; 
 
Ratio of jobs to workers (by sector, gender and working status at the study area level); 
 
Minimum and maximum acceptable employment rate and change in employment rate over time; 
 
Maximum allowable change in occupancy rate; 
 
Proportion of workers in each working age group; 
 
Proportion of excess households that relocate; 
 
Weighting of population / dwelling policy-led allocation of households (1 = fully dwellings led, 0 = 
fully population led, 0.5 = evenly weighted); and, 
 
Number of suppressed one person households that are reformed as multi-person households. 
Types of persons in households () 
This parameter is required for each control area to split the number of persons by age and gender into 
household types (one person or two or more person households). Table C-1 outlines the data sources used in 
the derivation of this parameter. 
Table C-1: Data sources for calculating parameter  
Data source 
Geography 
Description 
Census Table DC1109EW LAD 
Household composition in England & Wales by 
age and gender. 
Census Table DC1109SC Council Area 
Household composition in Scotland by age and 
gender. 
Census Table DC1104EW LAD 
All usual residents in England & Wales by age 
and gender. 
Census Table DC1104SC Council Area 
All usual residents in Scotland by age and 
gender. 
 
The household composition Census data (DC1109EW / DC1109SC) gives the total number of people by age 
and gender in each household type at control area level. However the age categories of the household 
composition Census data are not in the desired format for NTEM 7, therefore Census population data by age 
and gender (DC1104EW / DC1104SC) is used to proportionally split the household composition age 
categories.  

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
75
 
Household / dwelling differential () 
This parameter sets a maximum limit on the difference between the number of households and the number of 
dwellings at a study area level (i.e. how many more dwellings than households there may be). This ensures 
that the number of dwellings is not significantly greater than expected values. A value of 0.1 (i.e. 10%) has 
been assigned to this parameter. This value remains unchanged between NTEM 6.2 and NTEM 7. 
Minimum number of adults in households (min) 
This parameter sets a value for the minimum number of adults in a household at study area level. Although 
there are a very small number of zero-adult households in the Census, the value for this parameter is assumed 
to be 1. This value remains unchanged between NTEM 6.2 and NTEM 7. 
Maximum number of adults in households (max) 
This parameter sets a value for the maximum number of adults in a household at study area level. Due to the 
trend towards 1 person households, it is doubtful that the number of adults within a household shall exceed an 
average of two adults per household in any study area. However, to cover the possibility of this being the case 
when back-casting, the value of this parameter is set to 3 in order to catch any serious data problems that may 
occur when generating a scenario. This value remains unchanged between NTEM 6.2 and NTEM 7. 
Occupancy rate ()  
This parameter identifies an occupancy rate based on the number of households that occupy household 
spaces (i.e. dwellings) at control area level. There are occasional circumstances where more than one 
household can inhabit a dwelling. Table C-2 outlines the data sources used in the derivation of this parameter. 
Table C-2: Data sources for calculating parameter  
Data source 
Geography 
Description 
Census Table KS401EW 
LAD 
Total number of dwellings (household spaces) in 
England & Wales. 
Census Table KS401SC 
Council Area 
Total number of dwellings (household spaces) in 
Scotland. 
 
The total number of households and dwellings at control area level can be identified from the 2011 Census 
data (KS401EW / KS401SC). Subsequently, a calculation between the household and dwelling totals provides 
an occupancy rate for each control area. This parameters works in conjunction with the maximum allowable 
change in occupancy rate, and therefore varies over time as part of the model operation.  
Communal residents () 
This parameter identifies the proportion of communal residents in the total population by age and gender at 
study area level. ScenGen removes communal residents from the forecast population data, as it is only 
household population that is used. Table C-3 outlines the data sources used in the derivation of this parameter. 
This parameter is derived for the base year (2011), but applies only to forecast years. 
Table C-3: Data sources for calculating parameter  
Data source 
Geography 
Description 
Census Table LC1105EW MSOA 
All usual residents by residence type in England 
& Wales. 
Census Table LC1104SC 
DZ 
All usual residents by residence type in Scotland. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
76
 
Table C-4 & Table C-5 show the proportion of communal residents by age and gender within each study area. 
These figures are input into the Scenario Generator. 
Table C-4: Proportion of female population that are resident in communal establishments 
Study area 
0-15 
16-29 
30-44 
45-64 
65-74 
75-79 
80-84 
85+ 
East Midlands 
0% 
5% 
0% 
0% 
1% 
2% 
6% 
16% 
Wider South East 
0% 
3% 
0% 
0% 
1% 
2% 
5% 
15% 
North East 
0% 
3% 
0% 
0% 
1% 
3% 
7% 
17% 
North West 
0% 
4% 
0% 
0% 
1% 
3% 
6% 
17% 
Scotland 
0% 
4% 
0% 
0% 
1% 
3% 
7% 
17% 
South West 
1% 
4% 
0% 
1% 
1% 
2% 
5% 
16% 
Wales 
0% 
4% 
0% 
0% 
1% 
2% 
5% 
15% 
West Midlands 
0% 
4% 
0% 
0% 
1% 
2% 
5% 
15% 
Yorkshire & Humber 
0% 
4% 
0% 
0% 
1% 
2% 
6% 
16% 
 
Table C-5: Proportion of male population that are resident in communal establishments 
 
Dwelling vacancy levels () 
ScenGen requires information on dwellings available for the formation of a new household. The 2011 Census 
of Population no longer provides complete information on dwelling stock and second homes. Therefore the 
DCLG dataset22 on dwelling stocks were used, particularly “dwellings which have been unoccupied and 
substantially unfurnished for over six months”. 
Previous versions of NTEM used Census table CAS048, with estimated of unoccupied dwellings split into 
holiday accommodation and vacancy dwellings. This allowed estimation of dwellings not used as a main 
residence (S). This correction still exists in the mathematical formulation of the Scenario Generator, but is not 
                                                     
22 https://www.gov.uk/government/statistical-data-sets/live-tables-on-dwelling-stock-including-vacants 
Study area 
0-15 
16-29 
30-44 
45-64 
65-74 
75-79 
80-84 
85+ 
East Midlands 
0% 
6% 
1% 
1% 
1% 
2% 
3% 
9% 
Wider South East 
1% 
4% 
1% 
1% 
1% 
2% 
3% 
8% 
North East 
0% 
4% 
1% 
1% 
1% 
2% 
4% 
10% 
North West 
0% 
4% 
1% 
1% 
1% 
2% 
4% 
9% 
Scotland 
0% 
4% 
1% 
1% 
1% 
2% 
5% 
10% 
South West 
1% 
6% 
1% 
1% 
1% 
2% 
3% 
9% 
Wales 
0% 
4% 
1% 
1% 
1% 
2% 
3% 
8% 
West Midlands 
0% 
4% 
1% 
1% 
1% 
2% 
3% 
8% 
Yorkshire & Humber 
0% 
5% 
1% 
1% 
1% 
2% 
3% 
9% 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
77
 
required, and is therefore set to 0. It should also be noted that calculation of this parameter previously changed 
from NTEM v5 due to the treatment of student accommodation and communal residents. 
Ratio of workers to jobs () 
This parameter is responsible for converting jobs to workers. This is primarily due to the fact that workers are 
not constrained to having one job and occasionally have second jobs, intuitively more often in the case of part 
time workers and job sharing results in two workers sharing one job. This parameter is also used in the 
preparation of the base year data where jobs are required as input rather than workers derived from the 
Census.  
A set of these parameters are required by employment sector, gender and work type (full time/ part time) at 
study area level. Whilst derived for the base year (if not from the base year data), this parameter is applied 
only to forecast years. It should also be noted that it does not change at all in forecast years, which is consistent 
with previous versions of NTEM. Industry and study area specific parameters calculated in the previous version 
of NTEM were therefore retained for NTEM 7.  
Minimum and maximum acceptable employment rate and change in employment rate over time 
(min, max,ˆ ) 
This parameter sets a minimum and maximum threshold for employment rate and change in employment rate 
over time by age band and gender for a study area.   
Setting a minimum and maximum employment rate ensures that the employment rate lies within a certain 
threshold. Where the minimum / maximum threshold is breached, the program stops generating the scenario. 
This prevents the model from exceeding acceptable levels of employment.  
The proportion of working age population in employment is calculated from the NTEM 7 base population 
dataset by working status, age and gender at control area level. An employment rate in excess of the national 
average is required for those control areas that have higher than average rates. Differentiation by gender and 
working age group is also required. 
At control area level employment rates are as low as 4% for a given age and gender segment. However 
thresholds used in ScenGen are based on the minimum and maximum employment rates at study area level. 
Table C-6 shows the minimum and maximum employment rate parameters used in the Scenario Generator. 
These values are relevant for all forecast years, and therefore must be offer sufficient flexibility for changes in 
other demographic parameters. 
Table C-6: Forecast year minimum and maximum employment rates 
 
 
Gender 
Age group 
Minimum 
Maximum 
Female 
16-29 
50% 
85% 
Male 
16-29 
55% 
85% 
Female 
30-44 
55% 
99% 
Male 
30-44 
65% 
99% 
Female 
45-64 
50% 
90% 
Male 
45-64 
60% 
90% 
Female 
65-74 
2% 
50% 
Male 
65-74 
2% 
50% 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
78
 
The maximum change in employment rate over a specified period is also required. A value of 5% has been 
assigned to this parameter. Where this value is breached the generator will stop, indicating unrealistic data 
issues. This value remains unchanged between NTEM 6.2 and NTEM 7. 
Maximum allowable change in occupancy rate (max) 
This parameter sets the maximum rate at which the occupancy rate may change over the forecast period 
length. The maximum allowable change in occupancy rate is 0.5%, which is deemed to adequately cater for 
all forecast years as it is not anticipated that this rate will differ significantly over time. This value remains 
unchanged between NTEM 6.2 and NTEM 7. 
Proportion of workers in each working age group () 
This parameter determines the proportion of workers within each working age group (16-29, 30-44, 45-64 and 
65-74) by gender and working status (full-time / part-time) at study area level. Table C-7 outlines the data 
sources used in the derivation of this parameter. 
Table C-7: Data sources for calculating parameter  
Data source 
Geography 
Description 
Census table DC6107EW 
MSOA 
Working status by age and gender in England & 
Wales. 
Census table DC6107SC 
MSOA 
Working status by age and gender in Scotland. 
Census table DC6103 
Study area 
Communal resident economic activity by age 
and gender 
 
Working status at study area level can be identified from the 2011 Census data by age and gender (DC6107EW 
/ DC6107SC). As the base population data excludes communal residents, these are also removed from the 
dataset (DC6107EW / DC6107SC) to calculate the proportion of workers within each working age group. 
However this was only possible for England and Wales (DC6103EWla) as communal resident economic 
activity data does not exist for Scotland, therefore the economic activity of all residents has been used 
(DC6107SC).  
NTEM 6.2 contained working age segments only to 65. The total proportion of workers in the 16-64 age band 
was decreased over time to reflect expectations around a higher proportion of workers falling outside of this. 
For NTEM 7, a larger number of age bands have been included, in addition to an extension to 74. In the base 
year, a very small proportion of workers are present in the upper working age band (65-74). This has been 
gradually increased over time, at the expense of the preceding (45-64) age band. This is a general re-balancing 
of these two segments, and reflects general expectations of the increased proportions of older workers. As 
with NTEM 6.2, in the absence of robust data, only a gradual change has been applied. 
Proportion of excess households that relocate () 
This parameter identifies the proportion of excess households that relocate at control area level. 
Where household allocation within a control area exceeds housing supply, a proportion will be dispersed to 
other control areas with surplus dwelling supply within the same study area. A value of 0.5 has been assigned 
to this parameter, resulting in half of all excess households to relocate to other control areas where housing 
demand is unfulfilled. The remainder will be suppressed or in the case of one person households may coalesce 
to become multi-person households (see parameter ). This value remains unchanged between NTEM 6.2 
and NTEM 7. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
79
 
Population / dwelling policy-led development () 
This parameter determines whether the process of allocating households across control areas will follow 
dwelling availability or projected population trends.  A value of 0.75 is assigned to this parameter, erring more 
towards policy based forecasts than past trends. If the parameter is set to 1, this indicates total dependency 
on dwellings in relocating future households, whilst a value of 0 represents a total dependency on population 
projections. This value remains unchanged between NTEM 6.2 and NTEM 7. 
Suppressed single person households that convert to multiple person households () 
This parameter that determines the number of suppressed one person households that are reformed as multi-
person households at a global level. This occurs where households that wish to locate in a control area are 
unable to do so due to restricted dwelling supply. This models the real-world case where people elect to live 
with others where they cannot find a suitable dwelling on their own (e.g. parental home). A value of 3 is 
assigned to this parameter, indicating that every third single person household will convert to multi-person 
households. This value remains unchanged between NTEM 6.2 and NTEM 7. 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
80
 
Appendix D. Mathematic specification of 
methodology 
 
Notation and terminology 
The main variables and their definitions are shown in Table D-1. 
Table D-1: Quantity variables 
Variable 
Description 
E 
Employment (jobs including the self-employed). 
H 
Households. 
P 
Population (people) living in households - excludes those living in communal establishments. 
P = W + N. 
Pˆ  
Total population (input) – includes those in communal establishments. 
W 
Workers (working population) – full-time / part-time. 
N 
Non workers (i.e. students and others). 
D 
Dwellings. 
S 
Household spaces. 
 
Subscripts attached to a variable denote the time and space applicable to the variable (Table D-2), whilst 
superscripts denote categories or variable type (Table D-3). Forecasts are described as for the current year 
and scenario (t) for the period (p), starting from a previous year and scenario (t-1). 
Table D-2: Subscripts - time and place 
Subscript 
Description 
i 
NTEM zone. 
c 
Control area for forecasting tools - an aggregation of i for study area R at which the forecasts 
are controlled to exogenous projections. 
c=county level. 
H
c =district level for home / residential variables – all variables except E. 
E
c =county level for employment / workplace variables E. 
B 
Balancing Area,  i  B. 
R 
Study area to be processed = Region in most cases equivalent to the term “Definition” in the 
existing tool. 
t 
Forecast scenario - if forecast scenario is t, previous forecast scenario is (t-1). 
p 
Forecast period from scenario (t-1) to scenario t. 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
81
 
Table D-3: Superscripts – categories / dimensions 
Superscript 
Variable 
Category / dimension 
a 
E 
Employment sector (for trip attractions) as defined in Appendix B 
and Section 3.5 
g 
P, W 
Gender (Male, Female, Both –children). 
h 
H 
Modelled household types – 1 and 2+ person households. 
l 
P, W 
Modelled age bands (<16, 16-29, 30-44, 45-64, 65-74, 75+) (l for 
life stage). 
m 
P 
Age (input bands / years) of input data (m  l). 
w 
P, W, E 
Working status - only relevant for 16-29, 30-44, 45-64 and 65-74 
age groups. 
For variable P, w = F – full time, P – part time, S – students, O – 
other 
For variable W and E, w = F – full time or P – part time 
For variable N, w = S – students, O – other 
 
Other notation / assumptions applied are: 
n 
used to denote iteration n in an iterative procedure. 
^ 
used to identify user specified input data / parameters which may be internally adjusted during the 
forecasting process. 
~ 
used to denote intermediate values for variables / parameters. 
(1), (2) used in balancing process to denote many intermediate estimates of a variable 
Final forecasts / estimates have no hieroglyphics. 
 
For each of the main variables there are a more detailed set of categories as follows: 
 
Employment by sector, gender and working status (full-time / part-time), 
agw
ti
E
 
 
Population by gender, age and working status, 
glw
tiP
 
 
Households by size, 
h
ti
H  
 
 
 
 
 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
82
 
Parameter definitions   
The parameters and their definitions are shown in Table D-4. 
Table D-4: Subscripts - time and place 
Parameter 
Definition 
gm
R

 
The proportion of the population within a given gender and age group residing in communal 
establishments in a study area (region) R. 
max
min,

 
Acceptable range for the number of adults per household in a study area. 
ˆ  
Minimum number of persons allowed per 2+ person household. 
 
Acceptable difference in the ratio of households to dwellings in a study area (e.g. 0.25 
means within ±25%). 
c 
The occupancy rate (household spaces per dwelling) in control area c in the base year 
(2011). 
max


 
The maximum allowable increase in occupancy rate (households per dwelling) from the 
base year values. 
c
The vacancy rate in control area c in 2011 base year. 
min

The minimum allowable vacancy rate. 
 
The proportion of the vacancy rate from the previous scenario that the vacancy rate in the 
present scenario may fall to. 
Y
cS  
The number of household spaces in the control area c otherwise occupied (i.e. not vacant 
and not occupied by households – e.g. holiday homes) in the base year [currently set to 0]. 
 
The proportion of household locations that are dwelling / policy led rather than based on 
population trends. 
 
Determines the number of one person (type 1) suppressed households that are reformed as 
multi person households (type 2). 
hgl
c

 
The expected number of persons of type gl in household type h in control area c, from the 
2011 Census. 
agw
R

 
Ratio within employment sector a of workers of gender g and working status w (F or P), to 
jobs. S and O are irrelevant as they are non-workers. 
 
Population and household totals for study area (region)   
 
An initial estimate of total population is taken as: 
 
 



R
c
gl
tc
gl
tR
P
P
~
~
 and 






l
m
gm
R
gm
tc
gl
tc
P
P

1
ˆ
~
 
and c  R                                                                        (Eq 1)  
where 
gm
tc
Pˆ
 
Is population by gender g and age m for a control area c of study area R in scenario t. 
 
gm
R

 
Is the assumed proportion of the population in age group m of gender g residing in 
communal establishments in Study Area R. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
83
 
An initial estimate of total households is taken as: 
 
 


h
h
tR
tR
H
H
ˆ
~
 
 
                                                                                                                        (Eq 2) 
Job and worker totals for study area (region) 
Total number of jobs (employment) at the control area level in each forecast year is provided as an input. This 
information can be used to scale job totals from the previous scenario which are then aggregated to give study 
area totals. 
 
 












































agw
agw
c
t
agw
tc
agw
c
t
agw
c
t
agw
tc
agw
c
t
c
t
tc
c
t
agw
tc
E
E
E
E
E
E
E
E
E
E
)
001
.0,
ˆ
max(
ˆ
)
001
.0,
ˆ
max(
ˆ
ˆ
ˆ
~
1
1
1
1
1
1
 
where   


agw
agw
tc
tc
Eˆ
Eˆ
 and  



R
c
agw
tc
agw
tR
E
E
~
~
 
                                                                                  (Eq 3) 
 
A set of factors are derived to relate employment measured in units of jobs to population measured in units of 
persons in employment. This allows the derivation of the number of workers as follows: 
 
             


a
agw
tR
agw
R
glw
tR
glw
tR
E
W
~


 
                                                                                     (Eq 4) 
 
where 
agw
tR
E~
 
The initial estimate of employment by sector a, gender g and working status w, for study 
area R in year t from ‘Eq 3’. 
 
agw
R

 
The assumed ratio of workers of gender g and working status w to jobs in employment 
sector a (stored parameter table that could be modified occasionally), for study area R. 
 
glw
tR

 
The proportion of workers by gender g and working status w who are in the working age 
groups l, for study area R in year t. 
Control total checks 
 
The relationship between the adult population and households is checked to ensure that the average number 
of adults (in this case aged 16+) per household is acceptable. 
 
 
max
tR
child
l,
g
gl
tR
min
H~
P~






  where  
1

min

  and 
min
max



                                                       (Eq 4a) 
With declining household sizes through time, a second check is prudent. This check calculates the average 
number of persons per 2+ person households for the study area as a whole. 
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
84
 
            
ˆ
Hˆ
Hˆ
P~
h
tR
h
tR
l,
g
gl
tR





2
1
   where  
2

ˆ
 (specified by user) 
                                                      (Eq 5b)  
 
The workforce required to satisfy the employment forecasts should also be compared with the potential 
workers available from the population forecasts. 
  
         
gl
max
,t
gl
tR
w
glw
tR
gl
min
,t
P~
W




   where  
1
0



gl
t
gl
t
max
,
min
,


                                                 (Eq 6) 
Ratio of workers to jobs:   

agw
agw
tR
glw
glw
tR
E
W
~
 
                                                                                                            (Eq 5) 
The forecast number of households is checked to ensure that it will fit within the forecast development of 
dwellings. 
 
 

















R
c
Y
c
R
c
c
t
c
tc
tR
S
D
S





1
min
max
max
,
max
1
                                                       (Eq 6)  
where   

c
t 1


  is the vacancy rate obtained from the previous scenario and  


c
c
t



0
. 
 
The ratio of household demand to the supply of space is checked against the bounds specified and the result 
output to the log file for the user to check.   
 
              



max
~
tR
tR
S
H
1
 
                                                                                                                                 (Eq 9)
 
  
Households by control area 
The allocation of population and households to the control areas starts with trend based forecasts and then 
adjusts these to reflect the dwelling allocations provided by the regional planning bodies. 
The first step is to disaggregate study area household projections using input population forecasts for control 
areas. This assumes the change in household size is uniform for all control areas within the study area, but 
that the starting pattern of household sizes is retained. 
The previous total household estimates are scaled by the input change in population and controlled to the input 
Regional total 
 




















R
c
gl
gl
c
t
gl
gl
tc
c
t
gl
gl
c
t
gl
gl
tc
c
t
tR
tc
P
P
H
P
P
H
H
H
1
1
1
1
~
~
~
1
                                                                                 (Eq 10a)  
 
These households must then be split into those with one person (h=1) and those with two or more persons 
(h=2). 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
85
 
The household size parameters for 2+ person households (h=2) are first adjusted in each control area to reflect 
household forecasts by size. Parameters for 1 person households remain unchanged. Those for 2+ person 
households are scaled based on the change in input data. The lower bound of 
2
ˆ 

 is also applied to the 
2+ person household size parameters. 
 
 



gl
h
c
gl
h
tc
1
1




 
 



gl
h
c
tc
gl
h
tc
A
2
2





  and  




ˆ
gl
gl
h
tc


2
 
The value of adjustment factor 
tc
A  is calculated iteratively with estimates of the number of 2+ person 
households (h=2) as follows: 
 






















gl
gl
h
c
h
tR
R
c
n
h
tc
n
tc
n
tc
ˆ
,
Hˆ
H
A
max
A
2
2
2
1
1


  and  


















gl
gl
h
c
n
tc
tc
gl
gl
tc
n
h
tc
A
H
P
H
1
1
~
1
2
2

                       (Eq 10b) 
The initial value for the scaling factor should be based on the change from the previous number of persons in 
an average 2+ person household to the new average number of persons in a 2+ person household. Again the 
lower bound should be applied. Thus: 
 
Starting with 
























































gl
gl
h
c
R
i
h
i)
t(
R
i
R
i
h
i)
t(
glw
glw
i)
t(
h
tR
h
tR
gl
gl
tR
tc
ˆ
,
H
H
P
Hˆ
Hˆ
P~
max
A
2
2
1
1
1
1
2
1
1


 
 
where 
hgl
c

 
Is the expected number of persons of type gl in household type h in control area c  
and 
h
tR
Hˆ
 
Is the number of households by type. 
            
tc
H 1
 
Is the estimated total number of households in the control area as calculated by Eq 10a 
This iterative process continues until  
5
10
1
1





n
i
1
n
i
A
A
. 
Once  


2
1

h
tc
H
 has been obtained from this iterative process, the number of 1 person households is 
obtained by subtraction: 
 







2
1
1
1
1




h
tc
tc
h
tc
H
H
H
                                                                                                    (Eq 10c)  
In reality the change in dwellings supply will affect the distribution of households. An allocation of households 
purely based on the change in dwellings would be: 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
86
 
If  


0
1




pR
h
R
t
h
tR
D
H
Hˆ
 (i.e. dwellings and households are forecast to move in the same direction) then: 
 
     






pR
pc
h
R
t
h
tR
h
c
t
h
tc
D
D
H
H
H
H







1
1
2
ˆ
 
where  
     

c
t
tc
pc
D
D
D
1




  and  





R
c
pc
pR
D
D
 
                                                   (Eq 7)  
otherwise  











h
R
t
h
c
t
h
R
t
h
tR
h
c
t
h
tc
H
H
H
Hˆ
H
H
1
1
1
1
2







 
 
In practice the pattern of households is likely to be influenced by a mixture of trends and new developments, 
so a combination of the above two approaches seems appropriate: 
 
 
                 



h
tc
h
tc
h
tc
H
H
H
2
1
1





~
 
                                                                                            (Eq 8)  
 
In the absence of any data to determine the mix, the starting assumption is that 
75
.0


 
Finally it is necessary to evaluate whether there is an implied excess household demand over the supply of 
space, and then if appropriate, to reallocate the household demand across the control areas within the study 
area or to suppress excess demand.   
The number of household spaces using the initial vacancy rates and occupancy rates is: 
 
 




Y
c
t
c
c
tc
tc
S
D
S



1
1 

 
                                                                                                    (Eq 9)  
The maximum number of household spaces available in each control area can be calculated (as seen for the 
study area in Eq 8) as: 
 
 








Y
c
t
c
c
tc
tc
S
D
S










1
min
max
max
,
max
1
 
                                                      (Eq 10)  
where:  
 
The base year occupancy rate (household spaces / dwellings) 
c in control area c can change 
by a maximum amount 
max


 . 
 
The vacancy rate obtained in the previous scenario 

1

t
c

 in control area c can decrease by a 
maximum proportion  down to a lower bound 
min

 (1%) and 
c
c



0
. 
 
Y
cS  is the number of household spaces otherwise occupied (derived from the Census and taken 
as fixed). 
 
The adjusted vacancy rate for the scenario (required for future dependent scenarios) is calculated as: 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
87
 
 





















c
tc
tc
tc
t
c
t
c
ct
D
S
H






1
1
min
,
,
max
 
                                                                   (Eq 11)  
Where  
tc
H   is output by Eq 20. 
The housing pressure is then calculated as: 
 
max
~
tc
tc
tc
S
H
HPressure 
                                                                                                                 (Eq 12)  
If any control area in a given study area has excess households, i.e. 
1

max
~
tc
tc
S
H
 for any c, then identify spare 
capacity and reallocate a proportion of the excess households between the control areas within the study area 
based on the capacity available 
 
 
Excess households: 


0,
~
max
~
max
tc
tc
tc
S
H 

X
  and  



R
c
tc
tR
X
X
~
~
 
                                       (Eq 13)  
 
Capacity available: 


0,
~
max
max
tc
tc
H
S


tc
C
  and  



R
c
tc
tR
C
C
 
                                         (Eq 14)  
The user may specify whether all excess households are prepared to relocate within the study area or whether 
only a proportion of them will move – in which case the remainder will be suppressed.  If there is insufficient 
capacity even those identified as willing to relocate will be suppressed.  On this basis the revised estimates of 
households in each control area would be: 
Households by control area: 













tR
tc
tR
tR
t
tc
tc
tc
C
C
X
X
H
H
C
,
~
min
~
~

 
                                         (Eq 15)  
Where: 
t is the proportion of households willing to move to other parts of the study area.  When 
1

t
, 
this will lead to a reduction in the number of households from the input data.  Without an empirical basis for 
this proportion, initially assume that 
5.0

t
 (i.e. 50%). 
In practice we need to obtain revised household totals by type in each control area.  Thus the types of 
household being suppressed and relocating need to be estimated.   
 
It is likely that the inadequate space will impact differently on the different household types and as a result the 
suppression of households might be expected to alter the mix of household types in the study area.  On this 
basis the updated household forecasts by household type for each control area are estimated as: 
 
For household type 1:  

















































tR
tc
c
tc
h
tc
tc
t
tR
t
tR
t
t
tc
h
tc
tc
h
tc
h
tc
C
C
H
H
X
X
C
H
H
X
H
H
~
~
~
1,
~
min
1
1
~
~
~
~
1
1
1
1






 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
88
 
And for household type 2:  






































tR
tc
c
tc
h
tc
tc
t
tR
t
tR
tc
h
tc
tc
t
tc
h
tc
tc
h
tc
h
tc
C
C
H
H
X
X
C
H
H
X
H
H
X
H
H
~
~
~
1,
~
min
~
~
1
1
~
1
~
~
~
~
2
1
2
2
2




 
 
 
 
                                                                                                                                                            (Eq 16)  
 
where  
h
tc
H~
 is the number of households from equation 12, and  
 determines the number suppressed households that are reformed as household type 2.  If =4, 
then for every 4 single person households suppressed, 1 multi-person household is formed.  Assume 
=4 initially as this is consistent with existing policy led forecasts. 
This will work so long as






1
tc
tc
H~
X~
, otherwise more 1 person households will be suppressed than exist.  
In this exceptional case the program will terminate with an error. 
The second term in the equations above is the proportion of the excess that is to be reallocated to other control 
areas with spare capacity.  The third term is the remaining proportion of the excess that are suppressed by 
altering household formation rates. The fourth term is the additional households being reallocated to the control 
area. 
The following summary of adjustments made is recorded to the log file: 
 
Excess households identified:

































tc
h
tc
R
c
tc
t
R
c
tc
h
tc
tc
t
R
c
h
tc
H
H
X
H
H
X
X
~
~
~
1
1
~
~
~
1
1
1




and  

































R
c
tc
h
tc
tc
t
R
c
tc
h
tc
tc
R
c
h
tc
H
H
X
H
H
X
X
~
~
~
1
1
1
~
~
~
1
2
2


 
 
Of which 






R
c
h
tc
h
tc
H
H
1
1
~
  and  






R
c
h
tc
h
tc
H
H
2
2
~
  were suppressed; and 
 











R
c
h
tc
h
tc
R
c
h
tc
H
H
X
1
1
1
~
  and  











R
c
h
tc
h
tc
R
c
h
tc
H
H
X
2
2
2
~
  were reallocated.  
 
Population by control area 
The trend based forecast population is an input for each control area defined. Household forecasts are 
adjusted to take account of these population trends, (optionally) the expected change in dwellings and to 
alleviate housing pressure. Since there is some reallocation of households amongst control areas within a 
study area, then there should be an associated reallocation of population. 
The input trend based population control totals are adjusted to reflect the household adjustment.  An estimate 
of the number of people by type in each control area to be reallocated is calculated from the initial trend based 
household estimate 

h
tc
H 1
 and the final household figures 
h
tc
H
  by type – taking into account how the 
household reallocation is converted into persons.   
The input household size parameters for the 2+ person households (h=2) were previously adjusted in each 
control area to reflect household forecasts (Eq 10b). Those for 1 person households remain unchanged. 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
89
 
We know the number of households which are moved when converting from a trend to a policy based forecast 
and we know those who move out of crowded control areas. So by applying the updated persons per 
household, the population moving out is: 
             



























1
0
1
,
X~
C
min
H~
H~
X~
,
H~
H
max
H
tR
t
tR
tc
h
tc
tc
t
h
tc
h
tc
h
tc


  and 



c
h
tc
h
tR
H
H
 



h
tc
hgl
tc
hgl
tc
H
P

 and 



c
hgl
tc
hgl
tR
P
P
 
The significant variations in persons per household between alternative control areas within the study area 
(Region) should be taken into account when revising the population forecasts.  The average household size 
for those moving within the study area (Region) is: 




h
tR
hgl
tR
hgl
tR
H
P
 
The population moving into control areas is thus: 



























tR
tc
c
tc
h
tc
tc
t
tR
t
tR
h
tc
h
tc
hgl
tR
hgl
tc
C
C
H~
H~
X~
,
X~
C
min
,
H
H~
max
P


1
0
1
  
Having accounted for the changing household size to determine the number of people moving into and out of 
each control area, the trend based population forecasts can be adjusted as:  
 






h
hgl
tc
h
hgl
tc
gl
tc
gl
tc
P
P
P
P
~
 
                                                                                                         (Eq 17)  
Processing growth factors 
The revised approach provides the user with two options: 
 
Weights based factoring – where all input EGFs must be of the same sign. The adjustment is then 
applied multiplicatively to ensure the control totals are met. Thus the relative difference between 
the input EGFs is retained. 
 
Trend based adjustments- where the input EGFs can be of mixed signs to reflect growth and 
decline. In this case the absolute difference between the EGFs is retained. 
Adjustment to input EGFs for weights based approach: 
This works in a similar way to previous versions of TEMPRO. An initial adjustment is made to the growth 
factors to achieve the required control area changes (note that for employment the totals are for each sector 
a – summed over gender and working status groups gw).  The adjusted growth factors would be calculated 
as: 
 
 











c
i
a
i
t
a
pi
a
pc
a
pi
a
E
g
E
g
0
0
1 ,
ˆ
min
,
~
ˆ
max
pi
g
   if  
0


a
pc
E~
  
and 











c
i
a
i
t
a
pi
a
pc
a
pi
a
E
g
E
g
0
0
1 ,
ˆ
max
,
~
ˆ
max
pi
g
   if  
0


a
pc
E~
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
90
 
where  









gw
c
i
a
i
t
agw
tc
a
pc
E
E
E
1
~
~
   
(in fact the ~ is redundant since 
a
tc
a
tc
E
E
~

 as it is only the gw split that is adjusted). 
A further adjustment is then required at the time of application if any of the adjusted growth factors within the 
Control Area is less than -1. 
For households the equivalent equations are: 











c
i
i
t
H
pi
pc
H
pi
H
H
g
H
g
0
0
1 ,
ˆ
min
,
ˆ
max
pi
g
   if  
0


pc
H
 
and 











c
i
i
t
H
pi
pc
H
pi
H
H
g
H
g
0
0
1 ,
ˆ
max
,
ˆ
max
pi
g
 
  if  
0


pc
H
 
where  









h
c
i
h
h
i
t
h
tc
pc
H
H
H
,
1
   
 
Adjustment to input EGFs for trend based approach: 
This approach maintains the absolute difference between EGFs and adds a constant to each EGF to ensure 
the required control area change is required. There is no differential treatment for mixed sign EGFs or where 
the sign of the input EGFs is different to the control area change required. The only adjustment made is to 
prevent negative volumes of activity in any zone in scenario t.   
In this case the adjusted growth factors are calculated as: 














c
i
a
i
t
c
i
a
i
t
a
pi
a
pc
a
pi
a
E
E
g
E
g
1
1
ˆ
ˆ
pi
g
 
For households the equivalent adjustment is: 
             













c
i
i
t
i
t
H
pi
pc
H
pi
H
pi
H
H
g
H
g
g
1
1
ˆ
ˆ
 
Population and households by zone 
The households in each control area are distributed to the zones within the control areas based on expected 
growth factors which are exogenously specified.  
So  






0
1
1
,
max
H
pi
i
t
ti
g
H
H



 
However we require households by type in each zone which can be forecast as: 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
91
 
 



















c
i
i
H
pi
i
t
i
H
pi
i
t
tc
A
g
H
A
g
H
H
0
1
0
1
1
1
,
max
,
max
h
h
h
h
ti
H
                                                                                             (Eq 18)  
Where the adjustment term 
iA  is required to ensure that the resulting number of households in each zone 
matches the number of households that would be obtained by applying the adjusted growth factors to the 
previous household total. This can be achieved iteratively (maximum of 100 iterations) as: 
 
 




























h
c
i
n
i
H
pi
h
i
t
h
i
t
tc
i
t
A
g
H
H
H
H
0
1
1
1
1
,
max
h
1
n
iA
  
until 
 
01
0
1
.



n
iA
1
n
iA
  and where  
1
0 
iA
 
A first zonal estimate of the population by gender and age is obtained by estimating the change in population 
by type from the change in households by type and then adding the expected change to the previous 
population.  Note that the 
hgl
tc

 values used here are those obtained once the household forecasts have been 
determined. 
 
 
















0,
max
~
1
h
gl
i
1-t
gl
ti
h
h
i
t
ti
hgl
tc
H
H
P
P

                                                                                (Eq 19)  
The first estimate must then be adjusted to ensure the input control totals on population are met.  Thus the 
population by zone is obtained as: 
 



c
i
gl
ti
gl
tc
P
P
P
P
~
~gl
ti
gl
ti
 to ensure 
gl
tc
c
i
gl
ti
P
P



 
                                                                                (Eq 20)  
The change in the zonal population from the previous scenario is: 
 
 


gl
i
tP
P
P
1




gl
ti
gl
pi
 
                                                                                                                      (Eq 21)  
The working status for the population within the zone is established as part of the balancing process for the 
demand for workers. 
Employment and workers by zone 
The allocation of employment from control areas to zones is carried out using two mechanisms: 
 
Dwelling-led distribution of population; and 
 
Independent of changes in dwellings and population, following exogenous employment growth by 
sector. 
The allocation of the population led employment is carried out using the same zonal growth factors as the 
household information and is then adjusted to match the control totals for the employment sector.  Thus zonal 
employment is initially calculated as: 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
92
 
 




i
t
ti
agw
i
t
agw
ti
H
H
E
E
1
1
1



                                                                                                                      (Eq 22)  
A single scalar adjustment is then applied to ensure the control totals are met 
 





c
i
agw
ti
agw
ti
agw
tc
agw
ti
E
E
E
E
1
1
~
~
  so that  


c
i
agw
tc
agw
ti
E
E
~
~
 
                                                                   (Eq 23)  
where  
agw
tc
E~
 
is the initial employment data for the control area from Eq 3. 
The input growth factors are adjusted to estimate the zonal employment as: 
 



















c
i
a
pi
agw
i
t
a
pi
agw
i
t
agw
tc
agw
ti
g
E
g
E
E
E
0
1
0
1
1
1
,
max
,
max
~
~
                                                                                 (Eq 24)  
Balance workforce and workers 
The balancing process is concerned primarily with the ‘working age’ population (i.e. 16-29, 30-44, 45-64 and 
65-74 age groups). It is assumed that the proportion of population aged 75+ will not be of sufficient significance. 
The balancing process ensures that: 
 
There are the correct number of workers of different types for the jobs available living within a 
given balancing area; and 
 
The number of workers in each zone does not exceed a specified maximum proportion of the 
resident population. 
The implied numbers of workers required in a balancing area in the scenario given are: 
 










B
i
a
agw
ti
agw
R
gw
l
tR
glw
tB
E
W
~
~
|


  for  w = F ,P                                                                                 (Eq 25)  
The change in demand for workers from the previous scenario is: 
 
 
            


glw
B
t
glw
tB
glw
pB
W
W
W
1




~
~
  for  w = F ,P                                                                                         (Eq 26) 
The changing employment profile through time should lead to a change in gender profile of the workforce in 
each employment sector. The Scenario Generator includes an internal adjustment of the assumed gender and 
working status split by employment sector. 
This internal adjustment process uses the following key concepts: 
 
Treating the previous population and the new population as two separate groups; 
 
Treating declines before and completely independently to growth – logically they can only be 
applied to the “previous” population groups; 
 
Using ‘existing’ profiles / employment rates from the previous scenario as an initial estimate of 
workers; 
 
Calculating maximum possible numbers of workers by type; 
 
Adjusting profile of workers demanded so no maxima are breached; and 
 
Determining how far it is necessary to move from the initial values towards the maximum values 
in order to satisfy demand. 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
93
 
Reduction in population and / or workers 
The first stage is to account for any decreases in workers due to decreases in population. This gives an 
intermediate population estimate: 
If            
0


gl
piP
  then 

















gl
i
t
glw
i
t
gl
pi
glw
i
t
glw
ti
P
P
P
P
P
1
1
1
1
  otherwise 


glw
i
t
glw
ti
P
P
1
1


 
                            (Eq 27) 
and  





B
i
glw
ti
glw
tB
P
P
1
1
 
                                                                                                                      (Eq 28) 
The second stage is required only if either (FT, PT or both) of the required workforce categories w for the 
gender and age group gl in the scenario t is less than in the previous scenario (t-1).  In this case the number 
of workers of type w must be reduced while retaining the total forecast population numbers by zone (from Eq 
31). This is achieved by simply scaling back the number of workers (by status) in each zone to match the 
control area total.  The non-workers are then increased by the same volume to retain the zonal population 
totals.   
Thus if    
0


w
pB
W
  then: 
 















B
i
P
F
w
gl
ti
P
F
w
gl
tB
P
F
w
gl
ti
P
F
w
gl
ti
P
W
P
P
,
,
,
,
1
1
2
 
  for w = F,P 
                                                      (Eq 29)  
At this stage the total population by gender and age remains as above so that: 
 






w
glw
ti
gl
ti
gl
ti
P
P
P
1
1
2
 
                                                                                                       (Eq 30a)  
With the non-working population (not required at this stage) being the difference:  
 











P
F
w
P
F
w
gl
ti
gl
ti
O
S
w
gl
ti
P
P
P
,
,
,
2
2
2
 
                                                                             (Eq 34b)  
This gives an intermediate forecast of population by working status 
glw
ti
P 2
 accounting for decreases in 
population and / or workers.   
If  
0


gl
piP
 and 
0


w
pB
W
 then there are no decreases in population or workers. In this case the starting 
population data are the previous scenario results. To simplify notation for remaining balancing process, set 
            


glw
i
t
glw
ti
P
P
1
2


. 
Initial estimates of working status profile of population in each zone 
 
For this part it is proposed to treat the population in two groups: 
 
The existing residents – from the previous scenario (year) with any declines already subtracted – 
a group for which the working status w is known: 
glw
ti
P 2
 
 
The change in population in period p (from (t-1) to t): 


gl
i
t
gl
ti
gl
pi
P
P
P
1




  for which the working 
status w has yet to be forecast. 
An initial estimate of the population profile can be obtained by assuming the “new” population takes on the 
same profile as the existing population: 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
94
 
Thus  













gl
ti
glw
ti
gl
pi
glw
ti
glw
ti
P
P
P
P
P
2
2
2
3
~
  for w= F, P 
                                                                   (Eq 31) 
If there is no existing population, the new population is split equally besewn FT and PT workers. 
The adjusted initial estimate of workers for zone and the balancing area is then calculated: 
 
 





glw
ti
glw
ti
glw
ti
P
P
P
max
,
4
,
3
~
min
3

 for w= F, P  
 





B
i
glw
ti
glw
tB
P
P
3
3
 for w= F, P  
                                                                                            (Eq 32)  
Maximum supply of workers by gender 
For the new population the maximum rate is the specified ceiling rate. Thus: 
Maximum employment rate for existing population in zone: 
 















gl
w
glw
i
t
glw
gl
ti
max
1
max
max
,
,
min




  
                                                                                (Eq 33)  
Or  


gl
gl
ti
max
max
,



  when there is no existing data 
Giving a maximum supply of employees: 
 


gl
ti
gl
ti
gl
ti
P
P
2
2
max
,
max
,


 
                                                                                                         (Eq 34)  
 
and a possible maximum for each working status type of: 
 





gl
ti
glw
glw
i
t
glw
ti
P
P
2
2
1
max
,
max
,
~






 
Although it may not be possible for both maxima by working status to be attained within the overall ceiling 
rate.  
i.e. it is possible that  



w
glw
ti
gl
ti
P
P
max
,
max
,
~ 2
2
 or  



w
glw
ti
gl
ti
P
P
max
,
max
,
~ 2
2
.   
The two estimates 
gl
ti
P
max
,
2
 and 
glw
ti
P
max
,
~ 2
 are used in separate stages of the remaining forecasting steps. 
For the ‘new’ population the maximum number of employees by gender is: 
New population: 

gl
t
gl
pi
gl
pi
P
P
max
,
max
,




3
 
                                                                                            (Eq 35)  
Thus the maximum workers in zone: 


gl
pi
gl
ti
gl
ti
P
P
P
max
,
max
,
max
,
3
2
3



 
                                         (Eq 36)  
Aggregating the results for all zones in the balancing area, gives a maximum number of male and female 
workers by age group: 
 
Maximum workers by gender in balancing area:  
 





B
i
gl
ti
gl
tB
P
P
max
,
max
,
3
3
   
                                                                                                         (Eq 37)  

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
95
 
The working status profile of the change in demand for workers (i.e. the new demand) is used to split the 
potential workers in the “new population” into the two working status groups: 
 
 












w
glw
pB
glw
pB
gl
max
,
pi
glw
max
,
pi
,
W
max
,
W
max
P
P
1
1
3
3
 
                                                                   (Eq 38)  
The maximum number of workers by gender and working status available in the population is then calculated 
as: 
 





glw
pi
glw
ti
glw
ti
P
P
P
max
,
max
,
max
,
~
3
2
4



 and 




B
i
glw
ti
glw
tB
P
P
max
,
max
,
4
4
                                          (Eq 39)  
Since the estimates of the maximum full and part time workers may sum to more than the physical maximum 
a final adjustment is required. 
To make this final adjustment the difference between the initial zonal estimates 
glw
ti
P 3
 and 
glw
max
,
ti
P 4
 are 
considered for the two cases w=F and w=P.  For the working status with the smallest difference between the 
initial estimate and the initial maximum the maximum value is taken.  For the other working status the 
maximum value is recalculated to ensure the overall employment rate is not breached.  Thus: 
If  


gl
max
,
ti
w
glw
max
,
ti
P
P
3
4


  then  

glw
max
,
ti
glw
max
,
ti
P
P
4
5

 
Else 




F
w
gl
max
,
ti
F
w
gl
max
,
ti
P
P



4
5
   if  












P
w
gl
ti
P
w
gl
max
,
ti
F
w
gl
ti
F
w
gl
max
,
ti
P
P
P
P







3
4
3
4
 
Else 





P
w
gl
max
,
ti
gl
max
,
ti
F
w
gl
max
,
ti
P
P
P




4
3
5
 
and vice versa: 




P
w
gl
max
,
ti
P
w
gl
max
,
ti
P
P



4
5
  if  
             












F
w
gl
ti
F
w
gl
max
,
ti
P
w
gl
ti
P
w
gl
max
,
ti
P
P
P
P







3
4
3
4
 
Else  





F
w
gl
max
,
ti
gl
max
,
ti
P
w
gl
max
,
ti
P
P
P




4
3
5
 
Checking / Adjusting the Gender Profile of the Workers Demanded 
Excess workers are: 




0,
3
~
max
,
max
,
P
F
w
gl
tB
gl
tB
gl
tB
P
W
Z



  and 


g
gl
tB
l
tB
Z
Z
 
                                       (Eq 40)  
Capacity for workers is: 





0
3
,
~
max
,
max
,
gl
tB
P
F
w
gl
tB
gl
tB
W
P
Y



 
                                                               (Eq 41)  
If 
gl
tB
l
tB
Y
Z 
, then modifying the gender profile will not completely resolve any mismatch and in this case 
gl
tB
l
tB
Y
Z 
 workers are lost from the working age population – assumed to be filled by the over 75s and by 
increased double jobbing. 
The final estimates of workforce by gender are:  
 


l
tB
gl
tB
gl
tB
gl
tB
gl
tB
Z
Y
Z
W
W
,
min
~



 
                                                                                            (Eq 42)  

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
96
 
Checking / adjusting the full-time / part-time split of workers 
The next step is to finalise the working status of the population in a balancing area. To start, it is assumed that 
the working status profile of the modified demand is the same as for the initial demand.  So demand for workers 
by working status is: 
 

gl
tB
glw
tB
gl
tB
glw
tB
W
W
W
W
~
~

1
 
                                                                                                                      (Eq 43)  
By this stage it is ensured that demand for male and female workers can be accommodated within the 
population supply. As before there is either an excess or capacity calculated as: 
 
Excess workers are: 




0
5
1
,
P
W
max
Z
glw
max
,
tB
glw
tB
glw
tB


  
and  


w
glw
tB
gl
tB
Z
Z
 
                                                                                                                               (Eq 44)  
Theoretical capacity for workers is:  




0
1
5
,
W
P
max
Y
gl
tB
glw
max
,
tB
glw
tB


 
                                         (Eq 45)  
In practice there may be less capacity than indicated by Eq 49 since the sum of the maxima for the two working 
status groups could in theory be greater than the available population. However since adjustments have 
already been made to the workforce demanded by gender and age group, balanced estimates of workforce by 
gender, age and working status can be calculated as: 
 
 



gl
tB
glw
tB
glw
tB
glw
tB
glw
tB
Z
Y
Z
W
W
,
min



1
                                                                                 (Eq 46)  
And a revised set of employment rates: 
 
gl
tB
glw
tB
glw
tB
P
W


 
 
                                                                                                                      (Eq 47)  
Allocations of workers to zones 
Required data: 
Workers to be accommodated in the balancing area are: 
glw
tB
W
 from Eq 50. 
Initial estimates of workers in the balancing area are 
glw
tB
P 3
  from Eq 36. 
Adjustment required to initial estimates to match demand: 

glw
tB
glw
tB
P
W
3

 
The maximum number of workers in the balancing area is 
gl
max
,
tB
P 3
 from Eq 41, with separate estimates of 
the maximum number of workers by status 
glw
max
,
tB
P 5
 being available from Eq 43.   
Maximum adjustment to initial estimates that is possible is then 

glw
tB
glw
tB
P
P
3
4

max
,
 
There are two possible cases: 
 
The workforce demanded is a greater proportion of the population than in the previous scenario 
(expected to be the norm) i.e. 

glw
tB
glw
tB
P
W
3

; or 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
97
 
 
The workforce demanded is a smaller proportion of the population than in the previous scenario 
i.e. 

glw
tB
glw
tB
P
W
3

 
If the proportion of the population required to work is less than in the previous population (i.e.

glw
tB
glw
tB
P
W
3

) 
the initial estimates can be scaled back and: 
 


glw
ti
glw
tB
glw
tB
glw
ti
P
P
W
W
3
3

 
If the proportion of population working has increased then the employment rate lies somewhere between the 
initial and the maximum employment rate: 


glw
max
,
tB
glw
tB
glw
tB
P
W
P
5
3


 
and the proportion of the possible adjustment that is required is:  



glw
tB
glw
max
,
tB
glw
tB
glw
tB
glw
tB
P
P
P
W
3
5
3




 
The zonal forecasts of workers by zone are calculated as: 
 





glw
ti
glw
max
,
ti
glw
tB
glw
ti
glw
ti
glw
ti
P
P
P
P
W
3
5
3





 
These two cases can be combined into the single equation: 
            







glw
ti
glw
max
,
ti
glw
tB
glw
ti
glw
tB
glw
tb
glw
ti
P
P
,
max
P
P
W
,
min
W
3
5
0
3
3
1










                                          (Eq 48)  
With the total number of workers being 


w
glw
ti
gl
ti
W
W
 
Non-workers by zone 
The non-working population is the difference between the workers and total population, whilst the split between 
students and others is assumed to remain the same as the previous scenario. 
 






gl
i
t
glw
i
t
gl
ti
gl
ti
glw
ti
N
N
W
P
N
1
1




  for w=S,O 
                                                                                            (Eq 49)  
Update employment forecasts for change in worker profile 
The input employment data by sector is obtained by aggregation across the gender and working status groups: 


gw
agw
ti
a
ti
E
E
~
 
The revised split is then estimated as: 
 












gw
l
glw
tB
l
glw
tB
agw
ti
l
glw
tB
l
glw
tB
agw
ti
a
ti
agw
ti
W
W
E
W
W
E
E
E
~
~
~
~
                                                                                 (Eq 50)  
 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
98
 
Appendix E. Working Futures Industry 
Types 
Industry ID 
Industry 
SIC 2007 Section 
SIC 2007 Division 
1 
Agriculture 
A 
01-03 
2 
Mining & quarrying 
B 
05-09 
3 
Food, drink & tobacco 
C 
10-12 
4 
Engineering 
C 
26-28 
5 
Rest of manufacturing 
C 
13-25,29-33 
6 
Electricity & gas 
D 
35 
7 
Water & sewerage 
E 
36-39 
8 
Construction 
F 
41-43 
9 
Wholesale & retail trade 
G 
45-47 
10 
Transport & storage 
H 
49-53 
11 
Accommodation & food 
I 
55-56 
12 
Media 
J 
58-60, 63 
13 
IT 
J 
61,62 
14 
Finance & insurance 
K 
64-66 
15 
Real estate 
L 
68 
16 
Professional services 
M 
69-75 
17 
Support services 
N 
77-82 
18 
Public administration & defence 
O 
84 
19 
Education 
P 
85 
20 
Health & social work 
Q 
86-88 
21 
Arts & entertainment 
R 
90-93 
22 
Other services 
S 
94-96 
 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
99
 
Appendix F. Source of dwelling 
trajectories 
Dwelling trajectories are required at the control area (local authority district level), as discussed in Section 4.4. 
There is no single source of data, and therefore projections are not consistent in terms of base, horizon or 
general definition. The robustness of this data source should be considered alongside the ultimate purpose of 
dwelling projections, as described in Section 2 and Appendix D. 
The table below shows the source of dwelling trajectory for each control area. The main source is the relevant 
AMR (Annual Monitoring Report / Authority Monitory Report). Due to changes in statutory reporting 
requirements, these are not available for all areas. Other sources listed are LDP (Local Development Plan), 
SHLAA (Strategic Housing Land Availability Assessment), LHS (Local Housing Strategy), UDP (Unitary 
Development Plan), CS (Core Strategy) and HLS (Housing Land Supply). In some cases, multiple sources, 
supplementary documents, updates or appendices have been used to inform the trajectory; in this case, the 
ultimate parent has been cited. 
Control area 
Source 
Report 
year 
Projection 
horizon 
Control area 
Source 
Report 
year 
Projection 
horizon 
Aberdeen City 
LDP 
2012 
2030 
Mansfield 
AMR 
2011 
2026 
Aberdeenshire 
LDP 
2015 
2026 
Medway 
AMR 
2014 
2029 
Adur 
AMR 
2014 
2031 
Melton 
AMR 
2013 
2036 
Allerdale 
AMR 
2014 
2028 
Mendip 
AMR 
2012 
2028 
Amber Valley 
AMR 
2011 
2025 
Merthyr Tydfil 
AMR 
2014 
2020 
Angus 
LDP 
2015 
2025 
Merton 
AMR 
2014 
2026 
Argyll & Bute 
LDP 
2015 
2024 
Mid Devon 
AMR 
2013 
2026 
Arun 
HIS 
2015 
2030 
Mid Suffolk 
AMR 
2014 
2031 
Ashfield 
AMR 
2014 
2024 
Mid Sussex 
SHLAA 
2015 
2031 
Ashford 
SHLAA 
2009 
2021 
Middlesbrough 
AMR 
2012 
2021 
Aylesbury Vale 
SHLAA 
2009 
2026 
Midlothian 
LHS 
2013 
2023 
Babergh 
AMR 
2014 
2031 
Milton Keynes 
AMR 
2012 
2026 
Barking & Dagenham 
AMR 
2013 
2029 
Mole Valley 
AMR 
2014 
2019 
Barnet 
AMR 
2013 
2029 
Monmouthshire 
LDP 
2014 
2021 
Barnsley 
AMR 
2010 
2026 
Moray 
LHS 
2013 
2022 
Barrow-in-Furness 
SHLAA 
2014 
2027 
Neath Port Talbot 
LDP 
2015 
2026 
Basildon 
AMR 
2010 
2027 
New Forest 
AMR 
2015 
2026 
Basingstoke & Deane 
AMR 
2014 
2027 
Newark & Sherwood 
CS 
2011 
2026 
Bassetlaw 
AMR 
2014 
2027 
Newcastle upon Tyne 
AMR 
2014 
2030 
Bath & NE Somerset 
SHLAA 
2014 
2030 
Newcastle-under-Lyme 
AMR 
2011 
2026 
Bedford 
SHLAA 
2010 
2021 
Newham 
AMR 
2011 
2027 
Bexley 
AMR 
2012 
2029 
Newport 
LDP 
2012 
2025 
Birmingham 
SHLAA 
2030 
2014 
North Ayrshire 
LDP 
2014 
2024 
Blaby 
AMR 
2012 
2026 
North Devon 
AMR 
2012 
2026 
Blackburn with Darwen 
AMR 
2013 
2025 
North Dorset 
SHLAA 
2011 
2026 
Blackpool 
SHLAA 
2014 
2027 
North East Derbyshire 
AMR 
2014 
2031 
Blaenau Gwent 
LDP 
2012 
2021 
North East Lincolnshire 
AMR 
2014 
2029 
Bolsover 
AMR 
2014 
2031 
North Hertfordshire 
SHLAA 
2014 
2031 
Bolton 
AMR 
2014 
2026 
North Kesteven 
LDP 
2014 
2026 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
100
 
Control area 
Source 
Report 
year 
Projection 
horizon 
Control area 
Source 
Report 
year 
Projection 
horizon 
Boston 
AMR 
2011 
2026 
North Lanarkshire 
LDP 
2012 
2017 
Bournemouth 
AMR 
2014 
2026 
North Lincolnshire 
AMR 
2013 
2026 
Bracknell Forest 
AMR 
2012 
2026 
North Norfolk 
AMR 
2013 
2026 
Bradford 
AMR 
2013 
2030 
North Somerset 
AMR 
2013 
2026 
Braintree 
AMR 
2014 
2026 
North Tyneside 
AMR 
2014 
2032 
Breckland 
AMR 
2014 
2026 
North Warwickshire 
AMR 
2012 
2028 
Brent 
AMR 
2014 
2026 
North West Leicestershire 
AMR 
2013 
2031 
Brentwood 
AMR 
2012 
2017 
Northampton 
AMR 
2009 
2026 
Bridgend 
LDP 
2012 
2025 
Northumberland 
SHLAA 
2014 
2029 
Brighton & Hove 
AMR 
2014 
2030 
Norwich 
AMR 
2014 
2026 
Bristol 
SHLAA 
2009 
2026 
Nottingham 
AMR 
2013 
2028 
Broadland 
AMR 
2014 
2026 
Nuneaton & Bedworth 
AMR 
2014 
2028 
Bromley 
SHLAA 
2013 
2025 
Oadby & Wigston 
AMR 
2014 
2026 
Bromsgrove 
AMR 
2014 
2030 
Oldham 
AMR 
2014 
2031 
Broxbourne 
AMR  
2014 
2019 
Orkney Islands 
HLS 
2014 
2016 
Broxtowe 
SHLAA 
2014 
2029 
Oxford 
AMR 
2014 
2026 
Burnley 
AMR 
2012 
2021 
Pembrokeshire 
LDP 
2012 
2021 
Bury 
AMR 
2013 
2029 
Pendle 
AMR 
2013 
2030 
Caerphilly 
LDP 
2010 
2021 
Perth & Kinross 
LHS 
2011 
2024 
Calderdale 
AMR 
2011 
2026 
Peterborough 
AMR 
2014 
2026 
Cambridge 
AMR 
2013 
2031 
Plymouth 
AMR 
2013 
2029 
Camden 
AMR 
2013 
2029 
Poole 
AMR 
2013 
2026 
Cannock Chase 
AMR 
2013 
2028 
Portsmouth 
SHLAA 
2014 
2027 
Canterbury 
AMR 
2014 
2031 
Powys 
LDP 
2013 
2026 
Cardiff 
LDP 
2013 
2019 
Preston 
AMR 
2010 
2025 
Carlisle 
AMR 
2014 
2030 
Purbeck 
AMR 
2007 
2017 
Carmarthenshire 
LDP 
2013 
2020 
Reading 
AMR 
2013 
2026 
Castle Point 
AMR 
2014 
2031 
Redbridge 
AMR 
2014 
2030 
Central Bedfordshire 
AMR 
2014 
2033 
Redcar & Cleveland 
AMR 
2014 
2029 
Ceredigion 
AMR 
2014 
2021 
Redditch 
AMR 
2014 
2030 
Charnwood 
AMR 
2014 
2028 
Reigate & Banstead 
AMR 
2012 
2027 
Chelmsford 
AMR 
2014 
2021 
Renfrewshire 
LHS 
2011 
2016 
Cheltenham 
SHLAA 
2013 
2031 
Rhondda, Cynon, Taff 
LDP 
2011 
2021 
Cherwell 
AMR 
2015 
2031 
Ribble Valley 
AMR 
2014 
2028 
Cheshire East 
AMR 
2013 
2029 
Richmond upon Thames 
AMR 
2015 
2026 
Cheshire West & Chester 
AMR 
2013 
2018 
Richmondshire 
AMR 
2014 
2028 
Chesterfield 
SHLAA 
2012 
2017 
Rochdale 
AMR 
2014 
2028 
Chichester 
AMR 
2014 
2029 
Rochford 
AMR 
2014 
2027 
Chiltern 
AMR 
2014 
2029 
Rossendale 
AMR 
2014 
2026 
Chorley 
AMR 
2013 
2026 
Rother 
SHLAA 
2013 
2028 
Christchurch 
AMR 
2013 
2027 
Rotherham 
AMR 
2014 
2028 
City of London 
AMR 
2014 
2028 
Rugby 
AMR 
2014 
2026 
City of Westminster 
AMR 
2014 
2028 
Runnymede 
AMR 
2013 
2026 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
101
 
Control area 
Source 
Report 
year 
Projection 
horizon 
Control area 
Source 
Report 
year 
Projection 
horizon 
Clackmannanshire 
HLS 
2011 
2019 
Rushcliffe 
AMR 
2013 
2028 
Colchester 
AMR 
2014 
2029 
Rushmoor 
SHLAA 
2015 
2029 
Conwy 
LDP 
2007 
2022 
Rutland 
AMR 
2014 
2026 
Copeland 
AMR 
2012 
2021 
Ryedale 
SHLAA 
2015 
2027 
Corby 
AMR 
2014 
2021 
Salford 
SHLAA 
2013 
2032 
Cornwall 
SHLAA 
2015 
2025 
Sandwell 
AMR 
2015 
2026 
Cotswold 
SHLAA 
2014 
2034 
Scarborough 
AMR 
2014 
2024 
County Durham 
AMR 
2011 
2021 
Scottish Borders 
LHS 
2012 
2017 
Coventry 
SHLAA 
2014 
2026 
Sedgemoor 
AMR 
2014 
2029 
Craven 
AMR 
2015 
2021 
Sefton 
SHLAA 
2013 
2027 
Crawley 
AMR 
2013 
2026 
Selby 
AMR 
2015 
2028 
Croydon 
SHLAA 
2013 
2025 
Sevenoaks 
AMR 
2014 
2026 
Dacorum 
AMR 
2015 
2031 
Sheffield 
AMR 
2011 
2026 
Darlington 
AMR 
2011 
2026 
Shepway 
AMR 
2014 
2026 
Dartford 
AMR 
2014 
2026 
Shetland Islands 
No data  
Daventry 
AMR 
2014 
2029 
Shropshire 
SHLAA 
2014 
2026 
Denbighshire 
HLS 
2015 
2020 
Slough 
AMR 
2014 
2026 
Derby 
AMR 
2012 
2028 
Solihull 
AMR 
2013 
2028 
Derbyshire Dales 
SHLAA 
2013 
2028 
South Ayrshire 
LHS 
2011 
2016 
Doncaster 
AMR 
2014 
2028 
South Buckinghamshire 
AMR 
2014 
2026 
Dover 
AMR 
2014 
2028 
South Cambridgeshire 
AMR 
2014 
2031 
Dudley 
AMR 
2015 
2026 
South Derbyshire 
AMR 
2014 
2026 
Dumfries & Galloway 
LDP 
2009 
2024 
South Gloucestershire 
AMR 
2014 
2027 
Dundee City 
LDP 
2014 
2024 
South Hams 
AMR 
2011 
2026 
Ealing 
AMR 
2013 
2028 
South Holland 
AMR 
2010 
2026 
East Ayrshire 
LHS 
2013 
2018 
South Kesteven 
AMR 
2014 
2026 
East Cambridgeshire 
AMR 
2013 
2031 
South Lakeland 
AMR 
2014 
2025 
East Devon 
AMR 
2011 
2026 
South Lanarkshire 
LHS 
2012 
2025 
East Dorset 
AMR 
2012 
2026 
South Norfolk 
AMR 
2014 
2026 
East Dunbartonshire 
LHS 
2013 
2018 
South Northamptonshire 
AMR 
2014 
2029 
East Hampshire 
AMR 
2013 
2028 
South Oxfordshire 
AMR 
2012 
2027 
East Hertfordshire 
AMR 
2012 
2031 
South Ribble 
AMR 
2014 
2026 
East Lindsey 
AMR 
2014 
2024 
South Somerset 
AMR 
2013 
2028 
East Lothian 
LDP 
2014 
2029 
South Staffordshire 
AMR 
2014 
2028 
East Northamptonshire 
AMR 
2013 
2021 
South Tyneside 
AMR 
2010 
2025 
East Renfrewshire 
LHS 
2012 
2024 
Southampton 
SHLAA 
2013 
2026 
East Riding of Yorkshire 
AMR 
2013 
2029 
Southend-on-Sea 
AMR 
2013 
2028 
East Staffordshire 
SHLAA 
2014 
2029 
Southwark 
HLS 
2014 
2029 
Eastbourne 
AMR 
2014 
2019 
Spelthorne 
AMR 
2014 
2026 
Eastleigh 
AMR 
2012 
2017 
St Albans 
AMR 
2014 
2029 
Eden 
AMR 
2013 
2025 
St Edmundsbury 
AMR 
2013 
2031 
Edinburgh, City of 
LDP 
2014 
2023 
St. Helens 
AMR 
2011 
2027 
Eilean Siar 
LDP 
2012 
2019 
Stafford 
LDP 
2014 
2031 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
102
 
Control area 
Source 
Report 
year 
Projection 
horizon 
Control area 
Source 
Report 
year 
Projection 
horizon 
Elmbridge 
AMR 
2014 
2026 
Staffordshire Moorlands 
AMR 
2014 
2026 
Enfield 
AMR 
2013 
2021 
Stevenage 
AMR 
2014 
2031 
Epping Forest 
AMR 
2013 
2021 
Stirling 
LDP 
2014 
2023 
Epsom & Ewell 
AMR 
2015 
2026 
Stockport 
AMR 
2010 
2026 
Erewash 
AMR 
2011 
2026 
Stockton-on-Tees 
AMR 
2014 
2030 
Exeter 
SHLAA 
2015 
2030 
Stoke-on-Trent 
AMR 
2014 
2026 
Falkirk 
LHS 
2012 
2033 
Stratford-on-Avon 
AMR 
2014 
2031 
Fareham 
AMR 
2015 
2026 
Stroud 
AMR 
2010 
2025 
Fenland 
AMR 
2008 
2013 
Suffolk Coastal 
AMR 
2013 
2028 
Fife 
LHS 
2015 
2028 
Sunderland 
AMR 
2010 
2025 
Flintshire 
UDP 
2006 
2026 
Surrey Heath 
AMR 
2014 
2028 
Forest Heath 
AMR 
2012 
2031 
Sutton 
AMR 
2014 
2024 
Forest of Dean 
SHLAA 
2013 
2026 
Swale 
AMR 
2014 
2031 
Fylde 
AMR 
2014 
2029 
Swansea 
LDP 
2011 
2025 
Gateshead 
AMR 
2014 
2030 
Swindon 
AMR 
2013 
2026 
Gedling 
AMR 
2014 
2028 
Tameside 
AMR 
2012 
2029 
Glasgow City 
LHS 
2011 
2025 
Tamworth 
AMR 
2011 
2026 
Gloucester 
AMR 
2011 
2030 
Tandridge 
AMR 
2014 
2026 
Gosport 
AMR 
2014 
2029 
Taunton Deane 
AMR 
2014 
2027 
Gravesham 
AMR 
2015 
2028 
Teignbridge 
AMR 
2014 
2029 
Great Yarmouth 
AMR 
2014 
2029 
Telford & Wrekin 
CS 
2007 
2026 
Greenwich 
AMR 
2013 
2028 
Tendring 
AMR 
2014 
2031 
Guildford 
SHLAA 
2014 
2031 
Test Valley 
AMR 
2014 
2029 
Gwynedd 
LDP 
2015 
2026 
Tewkesbury 
AMR 
2014 
2019 
Hackney 
AMR 
2012 
2026 
Thanet 
AMR 
2014 
2026 
Halton 
AMR 
2012 
2028 
The Vale of Glamorgan 
LDP 
2011 
2026 
Hambleton 
AMR 
2014 
2026 
Three Rivers 
AMR 
2014 
2026 
Hammersmith & Fulham 
AMR 
2014 
2029 
Thurrock 
AMR 
2014 
2026 
Harborough 
AMR 
2015 
2031 
Tonbridge & Malling 
AMR 
2014 
2021 
Haringey 
AMR 
2014 
2026 
Torbay 
AMR 
2013 
2031 
Harlow 
AMR 
2013 
2026 
Torfaen 
LDP 
2010 
2021 
Harrogate 
SHLAA 
2013 
2028 
Torridge 
AMR 
2011 
2026 
Harrow 
AMR 
2012 
2013 
Tower Hamlets 
AMR 
2013 
2028 
Hart 
SHLAA 
2015 
2032 
Trafford 
AMR 
2013 
2026 
Hartlepool 
SHLAA 
2015 
2030 
Tunbridge Wells 
AMR 
2013 
2026 
Hastings 
AMR 
2014 
2028 
Uttlesford 
AMR 
2013 
2029 
Havant 
AMR 
2014 
2026 
Vale of White Horse 
AMR 
2011 
2029 
Havering 
AMR 
2014 
2024 
Wakefield 
AMR 
2014 
2030 
Herefordshire 
AMR 
2014 
2031 
Walsall 
AMR 
2013 
2026 
Hertsmere 
AMR 
2010 
2027 
Waltham Forest 
AMR 
2014 
2031 
High Peak 
AMR 
2014 
2031 
Wandsworth 
SHLAA 
2009 
2014 
Highland 
LDP 
2010 
2029 
Warrington 
AMR 
2014 
2027 
Hillingdon 
AMR 
2013 
2026 
Warwick 
AMR 
2014 
2029 

NTEM Planning Data Version 7 
Guidance Note 
 
 
 
 
Atkins   Guidance Note | Version 3 | July 2016 | 5138121 
103
 
Control area 
Source 
Report 
year 
Projection 
horizon 
Control area 
Source 
Report 
year 
Projection 
horizon 
Hinckley & Bosworth 
AMR 
2013 
2026 
Watford 
AMR 
2013 
2031 
Horsham 
AMR 
2014 
2026 
Waveney 
AMR 
2014 
2025 
Hounslow 
AMR 
2014 
2030 
Waverley 
AMR 
2012 
2028 
Huntingdonshire 
AMR 
2015 
2036 
Wealden 
AMR 
2014 
2027 
Hyndburn 
SHLAA 
2009 
2021 
Wellingborough 
AMR 
2013 
2021 
Inverclyde 
LHS 
2011 
2025 
Welwyn Hatfield 
AMR 
2014 
2029 
Ipswich 
AMR 
2014 
2030 
West Berkshire 
AMR 
2014 
2026 
Isle of Anglesey 
HLS 
2015 
2019 
West Devon 
AMR 
2012 
2026 
Isle of Wight 
AMR 
2014 
2027 
West Dorset 
AMR 
2014 
2028 
Isles of Scilly 
AMR 
2012 
2020 
West Dunbartonshire 
LHS 
2011 
2019 
Islington 
AMR 
2013 
2029 
West Lancashire 
AMR 
2015 
2027 
Kensington & Chelsea 
AMR 
2015 
2026 
West Lindsey 
AMR 
2010 
2026 
Kettering 
AMR 
2013 
2021 
West Lothian 
LHS 
2012 
2028 
King’s Lynn & West Norfolk 
AMR 
2013 
2028 
West Oxfordshire 
AMR 
2013 
2027 
Kingston upon Hull 
AMR 
2014 
2030 
West Somerset 
LDP 
2012 
2032 
Kingston upon Thames 
AMR 
2015 
2029 
Weymouth & Portland 
SHLAA 
2014 
2028 
Kirklees 
SHLAA 
2013 
2029 
Wigan 
SHLAA 
2015 
2030 
Knowsley 
AMR 
2012 
2027 
Wiltshire 
AMR 
2011 
2026 
Lambeth 
HIS 
2015 
2026 
Winchester 
AMR 
2014 
2031 
Lancaster 
SHLAA 
2009 
2024 
Windsor & Maidenhead 
AMR 
2010 
2026 
Leeds 
AMR 
2013 
2028 
Wirral 
AMR 
2014 
2037 
Leicester 
SHLAA 
2014 
2029 
Woking 
SHLAA 
2014 
2029 
Lewes 
SHLAA 
2014 
2029 
Wokingham 
SHLAA 
2015 
2026 
Lewisham 
AMR 
2014 
2030 
Wolverhampton 
AMR 
2014 
2026 
Lichfield 
AMR 
2014 
2019 
Worcester 
AMR 
2013 
2030 
Lincoln 
LDP 
2014 
2026 
Worthing 
AMR 
2014 
2026 
Liverpool 
AMR 
2012 
2028 
Wrexham 
LDP 
2013 
2028 
Luton 
SHLAA 
2014 
2031 
Wychavon 
AMR 
2013 
2030 
Maidstone 
SHLAA 
2009 
2026 
Wycombe 
AMR 
2014 
2024 
Maldon 
AMR 
2012 
2023 
Wyre 
AMR 
2013 
2021 
Malvern Hills 
AMR 
2013 
2030 
Wyre Forest 
AMR 
2014 
2026 
Manchester 
SHLAA 
2009 
2021 
York 
AMR 
2011 
2031 
 
 
 

 
© Atkins Ltd except where stated otherwise. 
 
The Atkins logo, ‘Carbon Critical Design’ and the strapline 
‘Plan Design Enable’ are trademarks of Atkins Ltd.