Data and analytics

Liases Foras is the largest repository of organized and structured data on real estate in India

Data and analytics

Founded in 1998, Liases Foras began its journey with rating real estate projects. From the very beginning Liases Foras strived to collect structured data on real estate market through scientific and research based methodology. Over time Liases Foras has shaped up as the largest repository of organished and structured database on real estate sector in the country. Nuggets of primary data collected by our team acts like a goldmine of information for industry stakeholders. Touted as the foremost authority in real estate sector, unbiased data and analytics commissioned by us are trusted equally by banks, fund houses, financial institutions, developers as well as the government.

It was in 2003 that we embarked upon the process of primary data collection for property market research at a massive level. Maharashtra Chamber of Housing Industry (MCHI) asked to us carry out comprehensive mapping of residential, commercial and retail market segments in Mumbai Metropolitan Region (MMR) along with detailed study of existing and proposed infrastructure development in MMR.

Sensing immense potential in the real estate sector, HDFC asked us to capture property market data across top-6 real estate markets (Mumbai Metropolitan Region, National Capital Region, Bengaluru, Chennai, Hyderabad and Pune) in the country in 2006. We expanded our coverage to 26 cities in 2011 soon after Axis bank gave us the mandate to track realty market dynamics in tier-II cities as well besides the top-6 market.

We leaped forward yet again in 2014 when India’s apex financial institution for housing, National Housing Bank (NHB) asked us to track 100 census cities including all state capitals and smart cities. At present our coverage spans over 60 cities, 13,179 sub-localities, over 11,000 developers and 77,61,533 number of units. Our products empower a user to divide 13,179 sub-localities into 1,748 PIN codes.

(NHB) uses Liases Foras’ primary data and turnkey solution to prepare Housing Price Index (HPI) @ market prices for under construction properties. During formulation and implementation of Real Estate (Regulatory and Development) Act, 2016 ministry of housing and urban affairs relied upon our inputs.

To meet the growing demand of empirical data, Liases Foras has started accessing registration data too. We have also started maintaining secondary market data for rental and outright transactions with the help of online and intermediary sources. Using machine learning methods, data collected from multiple sources (primary, secondary and registration) is churned into meaningful insights for quick, yet efficient decision making.

Our competitive data-backed products such as Ressex, Desktop Feasibility Solution (DFS), CRYSTAL, Opportunity Identification for Home Loans (OPPS) and Desktop Valuation (DV) offer advanced high-tech mapping and automated analysis of overall geography, region, suburb, micro-market, municipal ward, PIN code. User also has the flexibility to draw his/her own catchment on Google Map through our products to assess strength and weakness at a single click. Through our products, stakeholders can compare and analyse production, performance (supply, unsold and transactions), price trends of different asset classes and budget segments of selected projects.

How our data and analytics can help:

  • Stakeholders can gain analytical results to understand their target group
  • Leverage analytics for business growth and in It was in 2003 that wecreased profitability
  • Spot emerging trends in order to formulate strategy in time
  • Cull out specific but highly accurate information to maintain competitive edge
  • Can integrate firsthand industry data with in-house tool and dashboard for quick decision making
Learn how we created NHB RESIDEX

QoQ change in sales ( % )

QoQ increase in sales
QoQ decrease in sales
QoQ no change in sales
Bihar Sikkim Punjab Andhra Pradesh Meghalaya Uttar Pradesh Rajasthan Haryana Karnataka Maharashtra Assam Arunachal Pradesh Mizoram Nagaland Kerala Madhya Pradesh Tripura Manipur Tamil Nadu Telangana Odisha Uttarakhand Delhi Pondicherry Chandigarh Dadra & Nagar_Haveli Chhattisgarh Jharkhand Jammu & Kashmir Himachal Pradesh Goa West Bengal Gujarat

All India residential sale and unsold

Data Source

Data Source Methodology Vintage (Time series) Data Size
Primary (Developers Supply) Data Through field surveys to the developers’ sites Since 2003 Tracking over 20,000 projects every quarter
Secondary (Resales) Data Listing data from portals, and brokers Since 2016 Over 10 lacs properties
Registration Data From registrar websites and offices Since 2016 Over 5 million transactions

Coverage

Information can be accessed at both macro (country, state, city, town) and micro level (micro market, ward, block, project and unit). Following cities are covered:

  • National Capital Region (NCR or Delhi region)
  • Mumbai Metropolitan Region (MMR)
  • Outer MMR
  • Pune
  • Outer Pune
  • Hyderabad
  • Outer Hyderabad
  • Nagpur
  • Lonavala
  • Bengaluru
  • Outer Bengaluru
  • Chennai
  • Kolkata
  • Ahmedabad
  • Aurangabad
  • Kolhapur
  • Nashik
  • Solapur
  • Goa
  • Indore
  • Chandigarh
  • Jaipur
  • Surat
  • Vadodara
  • Cochin
  • Kanpur
  • Lucknow
  • Thiruvananthapuram
  • Coimbatore
  • Mangalore
  • Raipur
  • Patna
  • Bhopal
  • Bhubaneswar
  • Jodhpur
  • Neemrana
  • Rajkot
  • Kota
  • Srinagar
  • Shirdi
  • Tirupur
  • Vizag
  • Karnal
  • Dehradun
  • Ludhiana
  • Guwahati
  • Vijayawada
  • Meerut
  • Alwar
  • Tiruchirappalli
  • Daman
  • Vapi
  • Pondicherry
  • Silvassa
  • Agartala
  • Shimla
  • Dhanbad
  • Ranchi
  • Jamshedpur
  • Sangli

Data Attributes

Residential

  • City Type
  • City
  • Suburb
  • Location
  • Area
  • Road
  • Developer Group
  • Developer
  • Project
  • Sub Project
  • Product Types
  • Qtr of Financial Year
  • Month & Year of Launch
  • Month & Year of Possession
  • Latitude & Longitude
  • Distance from CBD
  • Launched Price in PSF
  • Current Price in PSF
  • Base Cost of Unit
  • Months Inventory
  • Sales Velocity
  • Avg Unit Size
  • Carpet Area in Sqft
  • Saleable Area in Sqft
  • Super Built to Carpet Ratio
  • Sale During the Qtr
  • Sold in last 12 months
  • Gross Sold Till Qtr End
  • Unsold as on Qtr end
  • Gross Supply since inception
  • Supply Added During Current Qtr
  • Supply During the Qtr
  • Value of Stock Sold
  • Value of Stock Unsold
  • Efficiency Ratio
  • Construction Status
  • Construction Stages
  • Amenities
  • Updated Construction Photographs

Commercial

  • State
  • City Type
  • City
  • Suburb
  • Location
  • Area
  • Road
  • Developer Group
  • Value of Stock Unsold
  • Developer
  • Project
  • Sub Project
  • Type
  • Qtr of Financial Year
  • Month & Year of Launch
  • Month & Year of Launch
  • Latitude
  • Construction Status
  • Longitude
  • Parking Charges
  • Lock In Period
  • Maintenance
  • Loading
  • Out Right Rate
  • Lease Rate
  • Distance From CBD
  • Slab
  • Gross Sold Till Qtr End
  • Unsold as on Qtr End
  • Unsold as on Qtr End
  • Gross Supply Since Inception
  • Sale During the Qtr
  • Sales in last 12 months
  • Supply During the quarter
  • Supply added During Current qtr

Retail

  • Project name
  • Developer name
  • Sub-project details
  • Launch date
  • Average unit size
  • Occupancy Status (based on first lease)
  • Saleable to Carpet ratios
  • Price Sheet
  • Possession date
  • Construction stage
  • Lease and outright rates
  • Car parking
  • Maintenance
  • Lock-in period

Data Points Covered

  • Property Name
  • Developer Name
  • Property Type
  • Property size
  • Amenities
  • No. of rooms
  • Types of rooms
  • Property Location
  • Longitude
  • Latitude
  • Average size of rooms
  • Property Rent
  • Location Trends

Data Points Covered

  • Property Name
  • Developer Name
  • Property Type
  • Property size
  • Amenities
  • No. of rooms
  • Types of rooms
  • Property Location
  • Longitude
  • Latitude
  • Average size of rooms
  • Property Rent
  • Location Trends

Data Points Covered

  • Property Name
  • Developer Name
  • Property Type
  • Property size
  • Amenities
  • No. of rooms
  • Types of rooms
  • Property Location
  • Longitude
  • Latitude
  • Average size of rooms
  • Property Rent
  • Location Trends

Vintage

Liases Foras was the pioneer in bringing the real estate data analytics in India. We began mapping the exhaustive real estate of the city of Mumbai for the first time in 2003 and based on the increasing popularity of the database, in 2007 we expanded it to cover six tier one cities. This growth was followed by the addition of two more cities in 2010. By the year 2012 we had percolated into 48 more tier two and three cities. Thereon quarter on quarter the coverage was expanded to rapidly cover 60 census cities.

Primary Data Collection Methodology- Feet on street

Liases Foras collects developers supplies through primary survey. Mystery Shopping techniques are adopted. Data is gathered through personal visits to the sites. The coverage stands more than 90% of the universe. We adopt bottom-up approach where units sum up to define building, buildings sum up to define project, projects sum up to define locality, localities- suburb and suburbs- city. Each and every project is mapped across various data points. Data is then conjoined and the movement in inventory, sales (offtake), price and construction progress is assessed at every project's level.