Housing Need and Demand Assessment (HNDA) Tool Instructions (2026)

3. Step-by-Step Tool Guide

Stage 1: Future Need for Additional Housing Units

Demography Choices

1. Use the Official NRS Household Projections (2022-based)

The CHMA recommends using NRS’s household projections. These data should be considered sufficient to produce a range of broad estimates of future housing needs. There are 3 variants as set out below to allow for the best local fit and full details of the projections can be found on the NRS website. They include:

• Principal
• High Migration
• Low Migration

Default (core) setting = principal household projection

The new projections are constrained to the Scottish Government’s big surveys annually. This helps to maintain the accuracy of the projections in the years following the Census in a way not previously possible. Increasing the validity of the projections in this way means that fewer variants are required to account for degrees of uncertainty in the principal projection.

1(a) Create and Use Own Household Projections

This is very resource intensive. The CHMA do not recommend this approach. If a decision is made to do this, it must be based on robust evidence, the data must be added to the Tool and the methodology used should be written-up in the HNDA. It is strongly recommended that any methodology is checked with NRS.

1(b) Adjust NRS’s Household Projections

Any adjustment must be based on robust evidence and written-up in the HNDA. Users can adjust the household projections up or down and for one or multiple years.

Step 1

If using NRS’s projections, select which one from the drop-down menu.

Step 1(a)

If using own projections. Tick the box entitled ‘use own projections.’ A drop-down table will appear. Enter the data here (number of projected households each year). This will need to be changed to reflect that you need to change the base data to get the HMAs to update.

Step 2(a)

If, however, only adjusting NRS’s projections, select which projection to adjust from the drop-down menu.

In the yellow boxes enter a % increase or decrease for any or all years.

Stage 2: Existing Housing Need for Additional Housing Units

Existing Housing Need Choices

2. The Default Built into the Tool

The default existing housing need figure in the Tool comprises the number of homeless households in temporary accommodation and those households who are both overcrowded and concealed families (HoTOC). This represents the minimum existing need figure only based on national data sets. If local authorities decide to use HoTOC the expectation is that local authorities will augment HoTOC with additional items of existing housing need such as care and support needs, poor quality housing etc.

Equally, local authorities may decide not to use HoTOC (because it is a minimum existing need figure only) but use their own estimate including items such as homelessness, overcrowded households, concealed families, care and support needs, poor quality housing etc. In all these cases, there must be clear evidence that this existing need cannot be met in-situ and will require and additional unit of housing to be provided. Care must also be taken to avoid double counting across categories, for example, in relation to overcrowded households and concealed families and the method used to avoid double counting must be explained in the HNDA write-up.

Default setting:

= HoTOC switched on

= Default is affordability filter switched off i.e. all need goes to social rent. If switched on need will get split between the four tenures.

The affordability filter settings are explained further on.

2(a) Use Own Estimates of Existing Need

Local authorities may decide not to use HoTOC (because it is a minimum existing need figure) but use their own estimate including items such those shown below (this is not an exhaustive list):

• homeless households

(live homelessness applications, which also includes those in temporary accommodation)

• overcrowded households

(using the Bedroom Standard - if the number of people sleeping in the house and the number of rooms available as sleeping accommodation (that is rooms normally used in the locality as a bedroom or living room) mean that two people of the opposite sex have to sleep in the same room then the accommodation will be overcrowded)

• concealed families

(a household with more than one family, for example, George and Amy live at the same address as their daughter Emily and her husband and daughter. Because Emily is not a dependent child and has her own family, there are two families in this household).

• care and support needs

(where the existing housing stock cannot be adapted to meet these needs and only a new build can deliver this)

• poor quality housing

(where a property is Below Tolerable Standard)

Untick the ‘HoTOC’ box and enter in own existing housing need figure.

Default is affordability filter switched off i.e. all need goes to social rent. If switched on then the need will get split between the four tenures.

The affordability filter settings are explained later on.

2(b) Decide how many years to clear existing need

The default is set at 5 years reflecting the position in a number of previous HNDAs. The default position in the Tool is for existing need to be cleared over a 5 year period. However authorities can change this to however many years they consider to be appropriate for their local circumstance and where they can evidence this. Selecting a longer period will smooth the existing housing need over more years in the projection period, rather than it being concentrated in the first five years of the projection period.

Default (core) setting = 5 years

Step 2

If using the default for existing housing need as, tick the box called ‘Use HoTOC method.’ The HoTOC figure will then appear. All need will be assigned to social rented housing. However the expectation is that local authorities will use their own estimates of existing housing need as below.

Step 2(a)

If using own estimates of existing housing need, type the calculated estimate into the box called ‘Existing need.’

Un-tick the ‘HoTOC’ box.

If using the affordability filter, tick the box called ‘Use Affordability Model.’ This will apportion some existing to social rent but also to the other tenures choices; buy, rent privately, below market rent. If the affordability filter is unticked all need is apportioned to social rent.

Step 2(b)

Type in the number of years for clearing existing need into the box called ‘Existing (Cleared) Years.’

At this point:

• the Tool has estimated the total number of households who will require additional housing of some kind in future.
• this includes new households that will form in future and households with current existing housingneed.

In the next stage:

• the Tool will estimate how many of these household may afford to purchase in the market and how many may need rented property of some kind.

Stage 3: Affordability - Income Growth and Income Distribution

Income Growth and Distribution Choices

Decide which income data source to use

The Scottish Government (SG) commissioned Heriot-Watt University and the David Simmonds Consultancy to produce banded Local Level Household Income Estimates (LLHIE) for the years 2014, 2015, 2016 and 2018. The SG then used the LLHIE to produce income distribution estimates. No new estimates have since been commissioned.

The 2018 LLHIE estimates have been uprated to 2023 using the Gross Disposable Household Income (GDHI) which is produced by the Office of National Statistics (ONS). GDHI at 2018 is compared to GDHI at 2023 (both converted into GDHI per Scottish household for each local authority). This difference is then used to derive a scaling factor for each authority, and for Scotland, that can be used to uprate the LLHIE from 2018 to 2023.

Default (core) setting = Scottish Government Small Area Income Estimates (2023)

3(a) Decide how average (median) income might grow over the projection period (e.g. 2023-2043)

The Tool is pre-programmed with five income scenarios that are designed to offer the users a range of income growth scenarios for average (median) household income. The scenarios are as follows:

Moderate Real Terms Growth (Tool Core/Default)

Household income growth is assumed to grow at 2.5% per annum in nominal terms. Assuming that inflation is at 2% per annum over the forecast horizon (the Bank of England’s inflation target), then in the long run this scenario assumes that there is real-terms income growth of 0.5% per annum. This is approximately in line with the Scottish Fiscal Commission’s January 2026 forecasts, which predict that real terms disposable household income per capita will grow by an average of 0.4% per annum over the period from 2025 to 2030 (the final year of their forecast horizon), and also similar to the experience since the financial crisis (an average of 0.7% per annum over the period from 2007 to 2025).

Below Real Terms Growth

Household income growth is assumed to grow at 0.5% per annum in nominal terms. Assuming that inflation is at 2% per annum over the forecast horizon, then in the long run this scenario assumes that there is an annual real-terms fall in household income of 1.5% per annum. During the recent cost-of-living crisis, real-terms disposable household income per capita fell by an annual average of 1.3% over the 3-year period from 2019 and 2022.

Moderately Below Real Terms Growth

Household income growth is assumed to grow at 1.5% per annum in nominal terms. Assuming that inflation is at 2% per annum over the forecast horizon, then in the long run this scenario assumes that there is an annual real-terms fall in household income of 0.5% per annum.

No Real Terms Growth

Household income growth is assumed to grow at 2% per annum in nominal terms. Assuming that inflation is at 2% per annum over the forecast horizon, then in the long run this scenario assumes that there is no real-terms growth in household income.

High Real Terms Growth

Household income growth is assumed to grow at 3.5% per annum in nominal terms. Assuming that inflation is at 2% per annum over the forecast horizon, then in the long run this scenario assumes that there is real terms income growth of 1.5% per annum. Real-terms disposable household income per capita in Scotland rose by an average of 2.8% per annum over the period from 2000 to 2007, so this scenario assumes that growth moves closer to pre-financial crisis levels, and over a sustained period of time.

HNDA Practitioners should select those scenarios which best reflect what might happen to incomes in the local area in future years.

The Tool also allows HNDA Practitioners to develop and input their own local income growth scenario(s) if they consider this appropriate for their local circumstances. With respect to the Scotland-level growth rates cited above, it should be noted that local growth rates tend to be more variable than growth at the national level, e.g. reflecting the expansion or contraction of industries which are regionally concentrated (at the national level, such trends can be offset to at least some degree by countervailing trends in other areas).

If an HNDA Practitioner does develop their own scenario(s), it must be based on robust data and written up within the HNDA. Details of how to input scenarios are described further on.

3(b) Decide how the shape of the income distribution may change over the projection period (e.g. 2026-2046)

The Tool is pre-programmed with three income distribution scenarios to reflect, for a given growth rate in average (median) household income, different household income distributions. The scenarios are defined in terms of how household incomes at the 10th and 90th percentiles grow relative to median household income (the 50th percentile), where the median household income growth rate has been chosen as discussed in Section 3(a) above. The Tool then automatically calculates growth rates at intermediate percentiles using a pro rata adjustment.

Greater equality

The incomes of the least affluent (represented by the 10th percentile of the income distribution) increase more steeply compared to the incomes of most affluent (represented by the 90th percentile of the income distribution).

No change (Tool core/default)

The incomes of the least affluent (represented by the 10th percentile of the income distribution) and the most affluent (represented by the 90th percentile of the income distribution) increase at the same rate as median household income. The gap in the income distribution between the most and least affluent will not change over time.

Greater inequality

The incomes of the most affluent (represented by the 90th percentile of the income distribution) increase more steadily compared to the incomes of least affluent (represented by the 10th percentile of the income distribution).

Historically, this ratio has fluctuated without a clear trend. More recently, there has been a decline, but this co-incides with a methodological improvement in the underlying data collection (the Family Resources Survey data has been linked with administrative records on social security benefits provided by DWP and tax credits, which aims to correct for the known undercount of benefit income reported through the survey). It will take further years of data to understand whether this decline is due to data improvements or represents a change in the underlying trend. For this reason, the model default is set to “No change.”

3(c) Decide which part of the income distribution is of interest.

The Tool is set up to analyse affordability (incomes divided by house prices and rent prices) at the 25^th percentile of income, house prices, and rental prices.

The 25^th percentile has been chosen because, historically, this is seen to represent where First-Time-Buyers enter this housing market, where housing need and demand is most critical.

However, the Tool also allows users to examine another point in the income distribution if that is of interest. For example, the Tool is also set up to show what is happening to incomes at the 75^th percentile of the distribution i.e. the more affluent end.

Users can change either of these by typing other percentiles into the boxes provided.

Default (core) setting:

= 25th percentile of the income distribution

= 75^th percentile of income distribution

Step 3

Use b to select which income source to use.

Step 3(a)

Use the ‘Growth in income scenario’ box to select which income growth scenario to run.

If using own scenario, the data should be inputted on the worksheet called ‘IncomeScenarios.’ The place where data should be inputted by users is highlighted in orange. When this is done, the new income growth scenario will then appear in the drop-down menu.

Step 3(b)

Decide which parts of the income distribution are of interest. The core (default) is set at the 25^th and 75^th percentile.

If interested in different percentiles, type these directly into the boxes in the section called ‘select part of the income distribution.’

Step 3(c)

Use the drop-down menu to decide which scenario about the shape of the income distribution might be relevant to the local area in future.

If using own scenario(s), the data should be inputted on the worksheet called ‘IncomeScenarios.’ The place where data should be inputted is highlighted in orange. When the data have been entered, this scenario will now appear in the drop-down menu when returning to the ‘Scenario All’ worksheet.

Stage 4: Affordability - House Prices

House Price Choices

4. Decide how house prices may grow over the projection period (i.e. 2023-2043)

The Tool is pre-programmed with 5 house price scenarios that are designed to offer users a range of options. The year-on-year changes in average house prices for each scenario are shown in the HNDA Tool on the worksheet called ‘PriceScenarios.’

Users should select the scenario which best reflects what might happen to average house prices in the local area in future years. Users may include their own house price growth scenario(s) to reflect local circumstances should they wish. Details of how to input scenarios are described further on. Additional scenarios should be evidence-based and written-up in the HNDA.

When choosing a house price scenario, users may wish to refer to the UK HPI or Registers of Scotland house price statistics to examine long-run trends in house prices in their local area. Users may also wish to refer to official forecasts of house price growth to inform the scenario they choose. The Scottish Fiscal Commission produce a forecast of Scottish house price growth (this can usually be found in the ‘Tax – Supplementary Tables’ workbook produced at the time of the Scottish Budget), as does the Office for Budget Responsibility (for the UK).

The choice of scenarios includes:

Trend Growth (Core/Default)

House prices grow at 2%, in line with the Bank of England’s inflation target, which means that in real terms they are unchanged. This would continue the trend since the financial crisis, with the median Scottish house price being unchanged in real terms over the period from 2010 to 2025.

Moderately High

House price growth is 2.5% per annum, which if inflation is at the Bank of England inflation target of 2% would imply real-terms growth of 0.5%. This is similar to the average annual growth rate in real-terms median house prices in the 16 local authorities with the highest rates of house price growth since the financial crisis, of 0.7% per annum. It is also similar to the January 2026 Scottish Fiscal Commission forecast, which is for average (nominal) house price growth of 2.6% over the period from 2026-27 to 2030-31.

Moderately Low

House price growth is 1.0% per annum, which equates to a real-terms fall of 1% per annum if inflation is in line with the Bank of England target of 2% in the long term. This is similar to the growth rate of real-terms median house prices in the 16 local authorities with the lowest rates of house price growth over since the financial crisis, of -0.9% per year over the period from 2010 to 2025.

High

House price growth is 3% per annum, which if inflation is at the Bank of England inflation target of 2% would imply a real-terms annual increase of 1%. This is similar to the average annual growth rate of real-terms median house prices in the 8 local authorities with the highest rates of house price growth since the financial crisis, of 0.9%.

Low

House price growth is 0.5% per annum, which if inflation is at the Bank of England inflation target of 2% would imply a real-terms fall of 1.5% per annum. This is similar to the average annual growth rate of real-terms median house prices in the 8 local authorities with the lowest growth rates since the financial crisis, of -1.4%.

4(a) Set affordability criteria to decide a cut-off point for who can afford to buy in the market and who cannot (and will some form of rental accommodation)

The default setting in the Tool assumes that a household is suitable for home ownership provided that they could afford to purchase a house at the lower quartile (25th percentile) of the house price distribution.

The test for affordability is that the house price is no more than 3.7 times the household’s income. This is derived by dividing the mean loan-to-income ratio (3.1) for a First-Time-Buyer mortgage in Scotland in 2025/26 by the mean loan-to-value ratio (83.1%), using data from UK Finance.

The default (core) = 25th percentile house price; 3.9x lower quartile income. All households whose income is above the threshold which allows them to afford a lower quartile house price are considered to be suitable for home ownership.

The default (core) = 25^th percentile house price; 3.9x lower quartile income

All households whose income is above the threshold which allows them to afford a lower quartile house price are considered to be suitable for home ownership.

Users can adjust these affordability criteria based on local circumstances if they wish by typing new values into the ‘Percentile’ and ‘Income Ratio’ boxes. This needs to be evidence-based and written-up up in HNDA.

Users may set their own affordability criteria. Details of how to input these are described below. These should be evidence-based and written-up in the HNDA.

Step 4

Using the ‘Future house price scenarios’ box select a future housing price scenario using the drop-down menu.

If using own scenario data should be inputted on the worksheet called ‘PriceScenarios.’ The place where data should be inputted is highlighted in orange. When the data have been entered, this scenario will now appear in the drop-down menu when returning to the ‘Scenario All’ worksheet.

Step 4(a)

Select an affordability criterion (what income multiple would be required to purchase a lower quartile house) using either the default or own criteria.

If using own, type the house price threshold in the ‘percentile’ box and type the income multiple in the ‘income ratio’ box. The latter should also take into account the size of deposit and size of mortgage.

At this point:

• the Tool has estimated the number of households who will require some form of additional housing units in future.
• split this between those who may afford to purchase in the market and those who may need rental accommodation.

In the next and final stage:

• the Tool will estimate of those who may afford to purchase, how many will actually go on to do so, and
• of the remainder how many may afford private rent, below market rent or social rent.

Stage 5: Affordability - Rental Prices

Rental Choices

5. Decide of those who may afford to purchase in the market, what proportion will actually go on to do so.

The default in the Tool is set at 65%. This assumes, of those who can afford mortgage repayments, only 65% also have the deposit to actually go on to buy.

HNDA practitioners can change this % based on local circumstances should they wish. This can be done by inputting a different percentage into the worksheet called `Core Assumptions.’ This should be evidence-based and written-up in the HNDA.

The effect of increasing this percentage would be to increase the amount of housing need that would be met via owner occupation and reduce the amount that be met by the rental sector (PRS, below market rent and social rent). Lowering the 65% threshold would have the reverse effect.

The default (core) = Of those who may afford to purchase, 65% have enough deposit to go on to do so

5(a) Set two affordability thresholds to split the remainder of the need into three rental sectors.

The 1^st threshold determines those who can afford to rent in the private sector.

The 2^nd threshold determines those can afford below market rent.

The remainder of the need (those who may not afford the private rent or below market rent) are apportioned to those who may afford social rent. This falls out of the above calculation and does not require a third threshold.

The Tool examines rent affordability by looking at income level in relation to both median and 30^th percentile market and social rents. Those with incomes nearer the 30^th percentile are more likely to need social rents and those nearer the median to afford private rents, with intermediate rent somewhere in between.

Whilst below market rent does refer to the need for some form of subsidized private rent, it may also be viewed as a potential indicator of demand for alternative shared equity products that are used to support home ownership. As such, the HNDA results may suggest that local authorities should potentially consider further analysis on this.

The default thresholds in the Tool are set as follows:

• if a household spends less than 25% of their income on rent the Tool assumes they may afford to rent in the private sector. This threshold has been used historically as the threshold for PRS affordability.
• if a household spends between 25% to 35% of their income on rent the Tool assumes they may be suitable for below market rent.

please note that the definition of below market rent is determined by how narrow or wide these thresholds are set by the users to reflect what might be appropriate for local circumstances. However, whatever this is, below market rent is always rent which is subsidised, in some way, below private rent but above social rent levels.

• if a household spends more than 35% of their income (including housing benefit) on rent the Tool assumes they may be suitable for social rent.

Users may choose to vary these two parameters. For example, if the two parameters were moved closer to each other, this would reduce the amount of need apportioned to below market rent, and in doing so, increase need apportioned to the PRS and increase need apportioned to social rent.

If HNDA Practitioners decide to change the thresholds this can be done by typing in two new % into the ‘Split Need into Tenure’ boxes on the ‘ScenarioAll’ sheet. This should be evidence-based and written-up in the HNDA Tool.

5(b) Decide how rental prices are likely to change over the course of the projection period e.g. 2026-2046.

The Tool is pre-programmed with five future rental price scenarios that are designed to offer the users a range of options. The scenarios are identical to the house price scenarios set out above, with the assumption being that over the long run rental prices are likely to be reasonably similar to trends in house prices (this is supported by comparing rental and house price changes since 2010). The Tool does though allow users to set different scenarios for house and rental prices, which Practitioners might want to use for purposes such as sensitivity testing. See Stage 4 for a discussion of the scenarios.

The year-on-year changes in rent prices for each scenario are shown in the HNDA Tool on the worksheet called ‘PriceScenarios.’

Users may input own rent scenarios. Details of how to input these are described below. Use of own rent scenarios should be evidence-based and written-up in the HNDA.

Step 5

Decide of those who could afford to buy in the market i.e. have sufficient income to secure a mortgage, what proportion have enough deposit to actually go on to buy.

If not using the default, enter a percentage into the box called ‘Proportion of Market who Buy.’ This should be evidence-based and written-up in the HNDA.

Step 5(a)

If not using the default, set the income threshold for ability to afford to rent in the private sector – use the box called ‘Upper i-rent income threshold.’

If not using the default, set the income threshold for those who may afford below market rent (and above which people may afford social rent) – use the box called ‘Lower i-rent income threshold.’

Step 5(b)

Using the ‘Rent Growth Assumption’ box, select a future rental price scenario using the drop-down menu.

If using own scenario, the data should be inputted into the worksheet called ‘PriceScenario.’ The place where data should be inputted is highlighted in orange. When the data have been entered, this scenario will then appear in the drop-down menu when returning to the ‘Scenario All’ worksheet. Any own scenarios should be evidence-based and written-up in the HNDA.

• The Tool has now finished running.

• It has generated an estimate of the additional, new housing units required in future to meet housing need and this has split total need into those who can afford:

  • owner occupation
  • private rent
  • below market rent
  • social rent

The next section explains how to find and interpret results.