India has started talking about Smart Cities. Over the past decade, focus had been on Urban Renewal and Slum Up-gradation. Be it smart cities or urban renewal / regeneration or slum up-gradation; Urban Housing proves to be one of the most difficult challenge for a town planner. Irrespective of who is providing housing, the Urban Local Body, the private developers / builders or a combination of both, housing gap assessment is necessary for all of the cases.

This note is an attempt towards the methodology for estimating housing gap for a city. Though the same may provide for a good assessment from the point of view of a research level thesis, it may need much more detailing for an urban planner in-charge of a city.

————————————————-

This is a simplistic housing gap assessment method and does not incorporate the complexities of land use dynamics, transportation impacts, node-based costing and real estate market dynamics in terms of distances. The integration of same would make it much more complex. The same may be done depending on how much time and resources are available to collect authentic as well as reasonable quantity and quality of data.

Assuming that we are well aware of the city in question, its demography and growth dynamics, we may calculate the housing gap as follows (two options are provided based on extent of data available).

Important Points to Remember

• Simple trend-line extension should not be the population forecasting method, for housing demand the accuracy of population forecasting is of high importance. Especially in case of million plus cities migration would play an extremely important role
• If we have to use trends lines extension, in any case, we should have data for at least past 40 years for forecasting population for next 20 years
• Classify population on the basis of household size and household income categories / affordability
• Understand the expenditure pattern [understanding the expenditure on rent / house purchase EMIs (Equated Monthly Installments) for a typical family would provide for a good insight into rented / self occupied housing] (optional at this level)
• Define income categories as High Income Groups (HIG)/ Middle Income Groups (MIG)/ Low Income Groups (LIG) and their sub classifications (based on above judgement) preferably use standard categories as arrived at from literature review to make it less challenge-able
• Understand real estate market in terms of Studio Apartments, 1 Room Kitchen (RK), One Bedroom Hall Kitchen (1 BHK), 1½ BHK, 2 BHK, 2½ BHK ….. 5-6 BHK and Bungalows, Plots etc.
• Understand cost implications of above and fit them into typical income categories as defined above and possible changes due to rise in real-estate prices / improved income categories
• Preferably data analysis and forecasting should be undertaken considering each of these categories separately
• Support population can be calculated by following e.g. – Say 100 people migrate into a city for equivalent job generation, then they need 10% of additional population (10 additional people) to support them as formal / informal sector (generally the next lower level of income group onward). These 10 additional people need another 10% to support them, and so on. This needs to be calculated till the last 10% value becomes insignificant in context of the whole calculation

[the fig. 10% is an example, the support population percentage is different to different cities and scenarios, for e.g. If a Greenfield city is being developed the percentage may rise to 50%-60% of original job generation; in case of a city predominantly bases on the tertiary / service sector for like IT / ITES or tourism etc. the values may rise further and in some cases as high as 250%. So the same needs to be arrived at based on literature study.]

• Vacant Stock are those houses that might have been sold to an investor but are not on rent, might not have been sold due to non-affordability, might be additional houses that a person maintains for weekends, trips, or may be seriously damaged or unfit for use etc. Literature review or interaction with local authority may indicate the ongoing vacancy rate for the city in question
• A range of 4% to 6% is generally an acceptable vacancy rate for a medium sized city. Though, for a medium size city in proximity to a major economic centre, the dynamics may be different (different cities have different vacancy rates for e.g. Ahmedabad, Surat, Baroda vary from ~22%, ~11% to ~16% respectively, whereas Hyderabad is only 3%)

Option 1 (Less Data Available)

Data Available / Required:

• Total number of houses – classified by permanent, semi-permanent, temporary structures
• Age of buildings (permanent structures)
  • > 75 years (dilapidated)
  • 50-75 years (possibly redevelopment or dilapidated)
  • 25-50 years (possibility converted to commercial / redevelopment)
  • 5-25 years (possibility of conversion to commercial)
  • < 5 years or Fresh
• Vacancy Rate
• Household size
• Defined income categories for households as HIG / MIG / LIG / EWS and their sub classifications (based on census data)
• Net migration (includes all income categories)
• Growth rate of the population
• Capacity to construct for the city (based on literature review), the same would increase over a period of time as technology for construction improves

 Step 1: Population Forecasting (Year-wise)

• Demographic assessment of city
  • Natural growth
  • Migration
• Arrive at total population forecast (including natural growth and net migration) for each and every year for next 20 years
• Calculate the total no. of Households for each year for the study period using household size

 ‘This value is the Total Number of Houses required for each year.’

 Step 2: Housing Demand

• Total Existing Stock = Total no. of permanent houses available as on date
• Vacant Stock = Vacancy Rate * Total Existing Stock
• Backlog 1: (all non-permanent structures as on date to be eventually converted into permanent structures)
• Outgoing Stock 1: Dilapidated housing stock to be demolished for redevelopment (assume all buildings with age more than 75 yrs as part of this value) – calculate / forecast for each year)
• Assume Target Vacancy Rate: generally in the range of 4% – 6%

Following formula is to be applied for each year separately:

Housing Gap = [Total Number of Houses – Total Existing Stock – Vacant Stock + Backlog 1 + Outgoing Stock 1] * [1 + Target Vacancy Rate]

This is the total demand for additional housing that needs to be provided for in a year.

Step 3: Actual Housing Gap

Now there would always be a maximum number / rate at which construction can happen (capacity to construct houses/year)

If number of new houses constructed per year is less than the Housing Gap, there would be a backlog in construction (Backlog 2), else there would be an Additional Supply if number of new houses constructed per year is more than the Housing Gap. These values of Backlog or Additional Supply get carried forward to the next year.

Hence for each subsequent year (other than the base year) the Actual Housing Gap would be updated as follows:

Actual Housing Gap (Y_n+1) = Housing Gap (Y_n+1)+ Backlog 2 (Y_n) – Additional Supply (Y_n)

Wherein, if (Y_n) = Year 1 then (Y_n+1) = Year 2; or (Y_n) = Year 2 then (Y_n+1) = Year 3 and so on.

[if there is Backlog 2 in a particular year, then Additional Supply would be zero for that year, and vice – versa, there may also be a case wherein both are zero.]

Sub-categorize Actual Housing Gap based on income categories data (ratio).

 

Option 2 (Relatively More Data Available)

Data Available / Required:

• Detailed demographic data trends including migration data by classification
• Period of stay trends for migrated population / retention in city
• Household size trends by income categories for households as HIG / MIG / LIG / EWS and their sub classifications (based on census data)
• Total number of houses – classified by permanent, semi-permanent, temporary structures as well as slum categories
• Housing stock under construction
• Age of buildings (permanent structures only)
  • > 75 years (dilapidated)
  • 50-75 years (possibly redevelopment or dilapidated)
  • 25-50 years (possibility converted to commercial / redevelopment)
  • 5-25 years (possibility of conversion to commercial)
  • < 5 years or Fresh
• Vacancy Rate
• Occupied houses
• Growth rate of the population
• Capacity to construct for the city (based on literature review), the same would increase over a period of time as technology for construction improves

 Step 1: Population Forecasting (Year-wise)

• Demographic assessment of the city
  • Natural growth
  • Migration
    • Migration from within greater urban agglomeration
    • Migration from within state / province
    • Migration from rest of country
• Classification of migrated population in terms of period of stay in the city
• Understand conversion factor from Temporary Residency to Permanent Residency (result in additional housing demand due to decision to purchase house) and possibility of temporary / permanent emigration
• Offset temporary and permanent emigration in terms of immigration
• Arrive at population forecast (category-wise for each and every year of the study period)
• The ratio of categories with reference to each other may vary and show a trend, if some population in one category is moving onto next income category due to the economic impact of a new or upcoming development / government initiative, account for the same
• Understand trends for household size for each category
• Forecast household size for each category based on literature review and trends
• Calculate the total no. of households for each category for each year for the study period based on population and household size

‘These values are the Total Number of Houses required for each category for each year.’

Step 2: Housing Demand (Category-wise as well as Year-wise)

• Total Existing Stock of housing based on above mentioned categories (in terms of 1 RK, 1 BHK, 1 ½ BHK, 2 BHK, 2 ½ BHK …. 5-6 BHK and Bungalows)
• Housing Stock under Construction [permanent structures only – category wise timelines for construction completion and availability for use]
• Backlog 1: All non-permanent structures need to be eventually converted into permanent structures. Classify Total Existing Stock (excluding slums) in temporary structures, semi permanent structures and permanent structures.
• Backlog 2: Number of households in regularized as well as non-regularized slums (by default classified as non-permanent)
• Outgoing Stock 1: Sanctioned housing stock from category wise timelines (land use conversion factor to be applied for conversion of residential to non-residential use)
• Outgoing Stock 2: Dilapidated housing stock / to be demolished for redevelopment (category wise timelines)
• Vacant Stock: = Vacancy Rate * Total Existing Stock (category wise)
• Assume Target Vacancy Rate: generally in the range of 4% to 6%

Following formula is to be applied for each year and each category separately:

Housing Gap = [Total Number of Houses – Total Existing Stock – Housing Stock under construction + Vacant Stock + Backlog 1 + Backlog 2 + Outgoing Stock 1 + Outgoing Stock 2] * [1 + Target Vacancy Rate]

This is the annual demand for housing that needs to be provided for a particular category in each year.

Step 3: Actual Housing Gap

Now there would always be a maximum number / rate at which construction can happen (capacity to construct houses/year)

If number of new houses constructed per year is less than the Housing Gap, there would be a backlog in construction (Backlog 3), else there would be an Additional Supply if number of new houses constructed per year is more than the Housing Gap. These values of Backlog or Additional Supply get carried forward to the next year.

Hence for each subsequent year (other than the base year) the Actual Housing Gap would be updated as follows:

Actual Housing Gap (Y_n+1) = Housing Gap (Y_n+1) + Backlog 3 (Y_n) – Additional Supply (Y_n)

Wherein, if (Y_n) = Year 1 then (Y_n+1) = Year 2; or (Y_n) = Year 2 then (Y_n+1) = Year 3 and so on.

[If there is Backlog 3 in a particular year, then Additional Supply would be zero for that year, and vice – versa, there may also be a case wherein both are zero.]

Step 4: Housing occupied on Rent basis (Optional)

Load the respective category wise ratio of temporary residents to total population to arrive at the total number of houses occupied for residential purpose on rented basis as part of the Actual Housing Gap.

Comments and suggestions from readers are requested and welcomed to improve on the forecasting methodology.