National Housing Bank (NHB)

National Housing Bank (NHB)

National Housing Bank (NHB) placed its trust in Liases Foras for designing the entire data architecture and digital workflow that goes behind publishing Housing Price Index for under construction residential properties. Out of 100 census cities, we have already created indices representing housing market in 50 cities are already available on NHB Residex portal. Using our advanced automation and data analytics, NHB has revamped the entire RESIDEX. The changed version of indices offer dynamic and interactive facilities which are simple and easy to navigate and come with downloadable features.

Revamped RESIDEX can help buyers and sellers to check and compare prices before entering a transaction. They can also analyse the price trends across different cities both at composite level and product category level. It helps lenders in credit evaluation. It provides promoters with a standardized tool to assess the housing demand. Government agencies can monitor trends in macro and micro markets and predict future behaviour of the housing market.

Panel of experts that comprise urban planners, IT professionals, data analysts, statisticians, GIS specialists, valuation experts and architects have brought solutions for data management including collection and extraction, cleaning and process automation, reverse geo-coding and mapping property within planning boundaries, zoning and regional segmentation for in-depth analysis. Researchers at Liases Foras have conducted through examination of international practices and statistical techniques to develop the concept and methodology for computation of HPI.

We have achieved the following mandate for NHB:

  • Shifting the base year to 2012 – 13
  • Expanding reach to all State Capitals and Smart Cities
  • Revising the residential segmentation for all the cities and managing database as per Zoning, Land Use Plans, Circle Rates, Stamp Duties, Mapping in Cities / States / Union Territories
  • Harmonising and correlating the index with HPI of the RBI
  • Introducing automation in the process of computation of the indices using advanced software to reduce processing time, improve accuracy and eliminate manual intervention, to the extent possible

In a sector which is highly heterogeneous, opaque and unorganised, developing granularised turnkey solution to represent the geography holistically in the form of an index was a huge challenge. The entire exercise required collecting primary market data through field visits and surveys, factoring in circle or ready reckoner rate, stamp duty, alongside prices quoted by independent brokers and agents. Before finalising algorithm to create the index, it was important to assign importance to each data set.

The two exhaustive tasks that we were expected to crack in a short span of time were:

Creating end-to-end automated enterprise solution

The entire process that goes behind data collation, data sorting and formulation of indices has been streamlined and automated to minimise needs of manual intervention and possibility of error. Liases Foras has created a workflow for the purpose of data collection and extraction from multiple sources and data cleaning to maintain highest level of accuracy during the preliminary analysis. Before converging data from varied sources and formats into a singular format, the enterprise solution created for NHB automatically detects anomalies and errors at the time of revalidation.

Finding contemporary methods to compute indices

Coming up with hedonic regression to determine importance of variety of factors and characteristics governing housing market was a tough nut to crack. While modified Laspeyres method was being used earlier, we decided to stick to weighted average prices before using Laspeyres formula to calculate Housing Price Index at Market Prices (under construction properties) and Housing Price Index at Assessment Prices (property value maintained by housing finance companies and institutions).

To arrive at right estimates, we changed the entire approach and decided to compute index as per weights assigned to different products. In previous versions of NHB RESIDEX, transactional weightages were assigned to boundaries/locations within a city. The practise limited the flexibility to assess prices at various regional boundaries without distorting the city level prices. Prices at city level should remain constant irrespective of the number of boundaries.

For example, Greater Mumbai has 24 Municipal Wards, 97 Census Wards/ Sections and 104 Pin Codes. Housing Price Indices of a city, when computed using weights of Municipal Wards would be different from Housing Price Indices computed based on weights of pin code.

As city prices cannot change with change in boundaries, we decided to do away with weightages of regional boundaries. Price should be the weighted average price irrespective of selection of boundary which may be a region, city, pin code, ward, a micro market or colony.

Hence weightages were based on number of transactions in the base year as per different product categories to compute revised RESIDEX. In addition to transactional weightages, factors using housing / population stock weights have been also applied at zonal and product levels.

The formula for computation is as: wherein, P0i = Price of i th product in base period Q0i = Quantity of Unsold stock/ Number of transactions of i th product in the base period P1i = Price of i th product in the current period n = Number of product types.

To make the entire process contemporary and attuned to popular credit schemes, product category classification is based on carpet area size under Credit Linked Subsidy Scheme (CLSS) guidelines. The three product categories are (a) units measuring less than or equal to 60 sqmt (b) units measuring more than 60 sqmt but less than or equal to 110 sqmt and (c) units measuring more than 110 sqmt. The main reason behind choosing this classification is to map the impact of affordable housing schemes that are being run by the government.

Method we follow to collect data

The projects are identified via secondary sources and then geo-mapped to ensure all under construction projects in the cities are duly covered. Post this, field surveys are conducted with surveyors visiting the identified projects. The data collated after thorough analysis includes unsold stock in units, their prices and construction status of the project. The data is updated every quarter. The price considered for computation is the base price which the developer offers to the consumer that excludes charges for floor rise, preferred location charge, car parking, government dues and other such charges. Cities for the calculation of HPI have been defined as per Census of India, 2011. Census has considered the municipal corporations, municipal councils, municipalities and nagar panchayats as separate cities/ statutory towns. City boundaries have been limited to boundaries defined by municipal corporations/ councils/ municipalities. Outgrown regions as defined in Census have not been considered as part of city.

Liases Foras is working closely with NHB to add entire spectrum of housing and related activities in the upcoming phases by creating Housing Rental Index (HRIs), Land Price Indices (LPIs) and Building Materials Price Indices (BMPIs), besides HPIs.

Indices being prepared by Liases Foras for NHB Residex