NHB Residex

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 achived the following mandate for NHB

In a highly unorganised and opaque sector, building a comprehensive index to represent the geography was challenging. It involved collecting primary market data through field surveys, incorporating circle rates, stamp duty, and broker-quoted prices. Assigning appropriate weight to each dataset was crucial before finalising the index algorithm.

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

Creating end to end automated enterprise solutions

The entire process of data collation, sorting, and index formulation has been automated to reduce manual intervention and errors. Liases Foras developed a workflow to collect, clean, and standardise data from multiple sources, with the NHB solution automatically detecting anomalies during revalidation.

1. Investigation or Feasibility Study

Investigation, analysis and study of data according to index type
2. Requirement Analysis

How much is the delay in the existing projects?
3. Design

Is sale volume good enough? Has the developer sold nearly 2.75% of the stock on a monthly basis in the last 12 months?
4. Development

Whether the cash generated from average monthly sales receipts of the last 12 months is good enough to meet the next month’s construction expenses. If yes, by how much do receipts exceed the expense; and if no, what is the percentage of shortfall?
5. Testing

Whether the cash generated from average monthly sales receipts of the last 12 months is good enough to meet the next month’s construction expenses. If yes, by how much do receipts exceed the expense; and if no, what is the percentage of shortfall?
6. Implementation

Whether the cash generated from average monthly sales receipts of the last 12 months is good enough to meet the next month’s construction expenses. If yes, by how much do receipts exceed the expense; and if no, what is the percentage of shortfall?
7. Maintenance

Whether the cash generated from average monthly sales receipts of the last 12 months is good enough to meet the next month’s construction expenses. If yes, by how much do receipts exceed the expense; and if no, what is the percentage of shortfall?

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 eg, 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.

Step followed to collect data

Projects are first identified via secondary sources and then geo-mapped to ensure comprehensive coverage of all under-construction projects in cities.

Field surveys follow, where surveyors visit identified sites to collect project-specific data.

Data collected includes: Unsold stock (in units), Base prices offered to consumers, Construction status of each project.

Data is updated quarterly.

The base price is used for index calculation. Base price excludes: Floor rise charges, Preferred location charges, Car parking costs, Government dues, Other additional charges.

Cities are defined as per the Census of India 2011. Census identifies cities/statutory towns as: Municipal Corporations, Municipal Councils, Municipalities, Nagar Panchayats.

City boundaries are limited to those defined by municipal bodies. Outgrowth areas, even if part of Census-defined urban agglomerations, are not included in the city definition for HPI.

Liases Foras, in collaboration with NHB, is working to expand the scope by developing additional indices: Housing Rental Index (HRI), Land Price Index (LPI), Building Materials Price Index (BMPI).