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 * Pricing
 * FAQs
 * Contact us
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VALUE YOUR PROPERTY NOW!

Submit

Find out the value of any residential property across 60 cities in India.




VALUE YOUR PROPERTY NOW!

Submit

Find out the value of any residential property across 60 cities in India.

INDIA’S OLDEST AND LARGEST
REPOSITORY OF REAL ESTATE DATA

At 4 levels, over 60 Indian Cities and with years of constant updation and
validation

1

PRIMARY DATA (DEVELOPERS SUPPLY)

Tracking of 20,000+ live projects through field surveys

2

SECONDARY DATA (RESALE)

Listing data of 1million properties

3

REGISTRATION DATA

Up-to-date mapping of 5 million legal transactions

4

RERA DATA

Data of all RERA registered projects

Here’s what
makes it
an easy to
use interface
 * MACHINE LEARNING ALGORITHM
 * RECOMMENDATION ENGINE OPTIMIZATION
 * URBAN PRICE SETTING MODEL
 * UNIFIED DATA STRUCTURE
 * MARKET VALUE ASSESSMENT

We have created a machine-learning algorithm to geocode property addresses based
only on address text.

Read More

To identify the most relevant comparable from millions of the records, we have
created a recommendation

Read More

Urban Price Setting Model” is a proprietary model developed by Liases Foras over
two decades

Read More

The Banks and HFCs also consider the guideline value of the property for
mortgage processing

Read More

The predictive model integrates car parking, floor rise, preferential location
charges (PLCs)

Read More
×
MACHINE LEARNING ALGORITHM

We have created a machine-learning algorithm to geocode property addresses based
only on address text. As shown in the figure, the algorithm predicts the most
appropriate geo- coordinates of the property based on the address of the
property.


×
RECOMMENDATION ENGINE OPTIMIZATION

To identify the most relevant comparable from millions of the records, we have
created a recommendation engine and ranking algorithm to show comparable in
descending order of relevance. It searches the comparable first in primary data
and then in the secondary data and makes the recommendations on an exact match
or in the absence of an exact match, engine recommends the price based on the
average of the top three and top five comparable.

×
URBAN PRICE SETTING MODEL

Urban Price Setting Model” is a proprietary model developed by Liases Foras over
two decades of research on real estate price dynamics.The model simulates the
price setting of an urban center and predicts the price of a property.The theory
describes that the price of a property is governed by four fundamental factors
such as Distance, Density (Economic Density), Surrounding, and the Product.A
multivariate regression equation that integrates these factors, predicts the
price of the property of differentiating product offerings and differentiating
geospatial characteristics with very high accuracy.

The rationale of the theory

There is a distinct price of the product (real estate) in a distinct space. The
same product is valued higher or lower with the dynamics of the space.

The quality of the product, the quality of demographic densities (neighborhood),
and surrounding (geographical attributes) get integrated with distance to
provide differentiating prices for differentiating products at the same vector
(location/space) and for the same product at different vectors (location/space).

The theory describes that every city maintains a specific price-setting at any
given point of time. The change in prices of one point influences the setting
and thereby impacts the prices of other points. And a new setting of the price
is formed.

That is the reason that rationality or irrationality, whatever be the state of
the market, it sets in the prices of all the points across the city at any given
point of time. There exists an equilibrium in the price setting.

×
UNIFIED DATA STRUCTURE

The Banks and HFCs also consider the guideline value of the property for
mortgage processing, for that we have created a unified data structure that
enables seamless integration of guideline values of different states in India.

×
MARKET VALUE ASSESSMENT

The predictive model integrates car parking, floor rise, preferential location
charges (PLCs), age discounts, terrace, and balcony area charges and provides
the composite value of the property. Since there is no uniformity in the super
built-up and carpet area loading, our recommendation engine suggests the
prevailing loading of the particular city and corresponding the specific time
era of the construction of the property. This utility is immensely helpful in
ascertaining the market value of the property.

Not just a price estimate but a professional valuation report
 * Comprehensive Valuation report- downloadable in pdf format
 * List of comparable projects from our primary and secondary data
 * Guideline Values (for Maharashtra, Telangana and Karnataka)
 * PCR – Price Correction Risk Analytics
 * Catchment Market Dynamics consisting of quarterly and annual trends of
   demand, supply, price, month inventory and sales velocity.


CLICK HERE TO VALUE YOUR HOUSE NOW


CLICK NOW

How it
works?
 * 1
 * 2
 * 3
 * 4
 * 5
 * 6

LOCATE YOUR PROPERTY

Locate your property by entering building name, road, or location or pointing on
the map.

FILL IN PROPERTY DETAILS

Fill in details like type of unit, size, loading, age or possession date etc.

CHOOSE COMPARABLE

Choose from inputs listed by machine algorithm from Liases Foras database. Added
feature of relevance score makes it even easier.

CHOOSE AVERAGE PRICE

Choose from a list of comparable properties. It may be a direct match, or an
average price of top 3 or top 5.

BASE PRICE COMPUTATION

A multivariate regression equation integrates the four critical parameters of
Distance, Economic Density, Surroundings and Product to arrive at base price.

FINAL COMPOSITE VALUE OF PROPERTY

Finally, DV arrives at a composite value that includes car parking charges,
floor rise, age discounting, PLC etc.


Pricing


BASIC

FREESIGNUP , IT'S FREE

 * Valuation of apartment, independent house, or a plot of land
 * Maintain Archive of Valuation Report


PREMIUM

INR 499 PER VALUATIONBUY NOW

 * Valuation of apartment, independent house, or a plot of land
 * Data of Comparable
 * Guideline Values
 * Market Dynamics
 * Price Correction Risk
 * Valuation Report in PDF
 * Maintain Archive of Valuation Report


ENTERPRISE

SUBSCRIPTION, PORTFOLIO OR BULK VALUATIONSCONTACT SALES

 * Annual Subscription
 * Valuation of bulk cases
 * Price Indexation

Call +91 98333 44500
View Sample Report

Note plus GST as applicable

* Please agree and accept the terms and condition before you pay

×

Use Cases

DESKTOP VALUATION OF AN ICONIC BUILDING IN MUMBAI SAMUDRA MAHAL, WORLI, MUMBAI

This video will showcase a valuation of a premium apartment building- Samudra
Mahal in Worli. Quite a few public figures are residing here.

Read More

DESKTOP VALUATION OF AN INDEPENDENT HOUSE.

Independent house in Aliganj Lucknow Liases Foras’ desktop has attempted one of
the major challenges of providing the valuation

Read More

DESKTOP VALUATION OF BULK CASES OR PORTFOLIO

Portfolio valuation is an extension of desktop valuation. Your entire portfolio
of 1000 or 10000 or more properties can be valued and updated periodically.

Read More

DESKTOP VALUATION OF A CELEBRITY BUNGALOW JALSA – AMITABH BACHCHAN’S BUNGALOW,
JUHU, MUMBAI

Desktop valuation can assist in conducting the valuation of premium properties.
This video will edify you on how DV can provide valuation of Amitabh Bachchan’s
Bunglow– Jalsa in Juhu.

Read More

DESKTOP VALUATION OF AN OLD APARTMENT IN, BANGALORE

In this video, valuation for an old apartment building – Dheeraj Manor,
Bangalore is steered. The building is around 16-17 years old.

Read More
prev
next

×
Desktop valuation of an iconic building in Mumbai Samudra Mahal, Worli, Mumbai

This video will showcase a valuation of a premium apartment building- Samudra
Mahal in Worli. Quite a few public figures are residing here. By putting the
accurate architectural details and quality parameters of the apartment, the tool
can provide precise valuation. Not only this, the tool also provides a price
correction risk and market dynamics. To know more please check out the video.

For any further queries, please write to us on contact@liasesforas.com

×
Desktop valuation of an independent house

Independent house in Aliganj Lucknow Liases Foras’ desktop has attempted one of
the major challenges of providing the valuation of independent houses, a
pre-dominant house type of most of the tier 2 cities in India.

Our predictive regression model which uses apartment rates and prevailing
economic density (income class of demographic) in the neighbourhood of the
property predicts the price of land and cost of constructed area. The valuation
model integrates the age of the building and provides the composite cost of the
independent house with remarkable accuracy.

Like the apartment valuation, a user has to provide the address of the property,
upon which the engine recommends the location of the property on the map. After
confirming the geo-coded location of the property, the user has to provide land
area and constructed area and age of the property in the tab mentioning
“independent house”.

After that, on pressing next the user gets the numerous comparable data of the
apartments in the catchment of the property. Here, the user has the option to
choose the method of average computation of the price of these comparable. And
finally, on pressing next, the user gets the composite value of the property.
Five easy steps provide the valuation of the independent house along with the
comparable.

For any further queries, please write to us on contact@liasesforas.com

×
Desktop Valuation of bulk cases or portfolio

Portfolio valuation is an extension of desktop valuation. Your entire portfolio
of 1000 or 10000 or more properties can be valued and updated periodically.

The valuation methodology used in portfolio valuation is the same as that used
in desktop valuation. The list of portfolio properties is maintained in an
excel/CSV.

A client is expected to provide below mentioned details for each property,

 * Full property address,
 * Type of the property (Resi / Comm or Apartment / House / Plot / Shop / Office
   etc.)
 * Property size in Sqft
 * Property size type (Carpet / Built-up)
 * Land size in case of House or plot properties

Once we receive the portfolio, it is checked for data consistency. For example,
the type of property should be one of the predefined types, Land size and
property size should be of “Number” type, etc.

All the property addresses in the portfolio are geocoded and checked for correct
latitude and longitude. This is a one-time process for a portfolio.

The portfolio file is then fed to the portfolio valuation API. The API searches
for the most relevant comparable and calculates the appropriate price of the
portfolio property. The same portfolio can be revalued at a quarterly frequency.

×
Desktop valuation of a celebrity bungalow Jalsa – Amitabh Bachchan’s Bungalow,
Juhu, Mumbai

Desktop valuation can assist in conducting the valuation of premium properties.
This video will edify you on how DV can provide valuation of Amitabh Bachchan’s
Bunglow – Jalsa in Juhu. This is one of the use cases where a celebrity
bungalow, which is one of the outliers in the market, can also be assessed
through this dynamic tool. To know more please check out the video.

For any further queries, please write to us on contact@liasesforas.com

×
3. Desktop valuation of an old apartment in, Bangalore

In this video, valuation for an old apartment building – Dheeraj Manor,
Bangalore is steered. The building is around 16-17 years old. The tool
deliberates the right set of comparables from the Liases Foras database to
assess the valuation of the property. It also considers age discount of the
property and provides accurate valuation. To know more please check out the
video.

For any further queries, please write to us on contact@liasesforas.com

Desktop Valuation For
60 Cities
See All Cities
Solutions Given

“MAJOR PAIN POINTS OF BANKS AND HFCS”

Some Institutions consider guideline value of the property for mortgage
processing?

Created a unified data structure which enables seamless integration of guideline
values of different states in India

5

What is prevailing demand supply and price trends in the micro-market and what
is risk rating of the micro-market?

We provide quarterly and annual trends o primary market of supply, demand and
prices along with location ratings

6

Institutions may not have correct address or latitude and longitude of the
properties?

Created a Machine Learning algorithm to geocode property addresses based only on
address text

1

To Identify most relevant 3-5 comparable properties out of millions of
properties from data base?

Created a recommendation engine and ranking algorithm to show comparable in
descending order of relevance

2

Actual price may vary depending upon the quality attributes of the buildings &
neighborhood?

Created a proprietary model of price prediction based on “Distance, Density and
Surrounding”

3

Institutions do not enough data to assess the price correction risk in
prevailing market value?

Created a model to assess the price correction risk and productive value for the
property

4

Some Institutions consider guideline value of the property for mortgage
processing?

Created a unified data structure which enables seamless integration of guideline
values of different states in India

5

What is prevailing demand supply and price trends in the micro-market and what
is risk rating of the micro-market?

We provide quarterly and annual trends o primary market of supply, demand and
prices along with location ratings

6

Institutions may not have correct address or latitude and longitude of the
properties?

Created a Machine Learning algorithm to geocode property addresses based only on
address text

1

To Identify most relevant 3-5 comparable properties out of millions of
properties from data base?

Created a recommendation engine and ranking algorithm to show comparable in
descending order of relevance

2
prev
next

×
 * Agartala
 * Ahmedabad
 * Alibag Region
 * Alwar
 * Amritsar
 * Aurangabad
 * Bangalore
 * Bhopal
 * Bhubaneswar
 * Chandigarh
 * Chennai
 * Cochin
 * Coimbatore
 * Daman
 * Dehradun
 * Delhi
 * Dhanbad
 * Dharuhera-Bhiwadi
 * Faridabad
 * Ghaziabad
 * Goa
 * Greater Mumbai Region
 * Gurgaon
 * Guwahati
 * Hyderabad
 * Indore
 * Jaipur
 * Jamshedpur
 * Jodhpur
 * Kanpur
 * Karnal
 * Kolhapur
 * Kolkata
 * Kota
 * Lonavla
 * Lucknow
 * Ludhiana
 * Mangalore
 * Meerut
 * Nagpur
 * Nashik
 * Navi Mumbai Region
 * Neemrana
 * Neral - Karjat Region
 * Noida & Gr Nodia
 * North - East
 * (Diva - Shahpur - Titwala) Region
 * Palghar - Boisar Region
 * Panvel-Uran Region
   (Outside Navi-Mumbai)
 * Patna
 * Pen Region
 * Puducherry
 * Pune
 * Raipur
 * Rajkot
 * Ranchi
 * Sangli
 * Shimla
 * Shirdi
 * Silvassa
 * Solapur
 * Srinagar
 * Surat
 * Thiruvananthapuram
 * Tiruchirappalli
 * Tiruppur
 * TMC (Thane) Region
 * Vadodara
 * Vapi
 * Varanasi
 * Vijayawada
 * Vizag
 * Western (Mira road - Virar) Region

 * Agartala
 * Ahmedabad
 * Alibag Region
 * Alwar
 * Amritsar
 * Aurangabad
 * Bangalore
 * Bhopal
 * Bhubaneswar
 * Chandigarh
 * Chennai
 * Cochin
 * Coimbatore
 * Daman
 * Dehradun
 * Delhi
 * Dhanbad
 * Dharuhera-Bhiwadi
 * Faridabad
 * Ghaziabad
 * Goa
 * Greater Mumbai Region
 * Gurgaon
 * Guwahati
 * Hyderabad
 * Indore
 * Jaipur
 * Jamshedpur
 * Jodhpur
 * Kanpur
 * Karnal
 * Kolhapur
 * Kolkata
 * Kota
 * Lonavla
 * Lucknow
 * Ludhiana
 * Mangalore
 * Meerut
 * Nagpur
 * Nashik
 * Navi Mumbai Region
 * Neemrana
 * Neral - Karjat Region
 * Noida & Gr Nodia
 * North - East
 * (Diva - Shahpur - Titwala) Region
 * Palghar - Boisar Region
 * Panvel-Uran Region
   (Outside Navi-Mumbai)
 * Patna
 * Pen Region
 * Puducherry
 * Pune
 * Raipur
 * Rajkot
 * Ranchi
 * Sangli
 * Shimla
 * Shirdi
 * Silvassa
 * Solapur
 * Srinagar
 * Surat
 * Thiruvananthapuram
 * Tiruchirappalli
 * Tiruppur
 * TMC (Thane) Region
 * Vadodara
 * Vapi
 * Varanasi
 * Vijayawada
 * Vizag
 * Western (Mira road - Virar) Region

About us

Liases Foras, founded in 1998, is the only non brokerage real estate research
company in India. Data and science form the core of our services, which range
from providing market intelligence and risk advisory to lenders and mortgage
companies, to providing development advice, best use, and valuations to
developers, funds, banks and corporations.

We are proud to be a strategic partner of the highly diversified DMG Group. The
group operates in 40 countries across the world and its revenue in 2014 was over
USD 3 billion. DMG information handles over 95% of the United Kingdom’s
valuation workflow for lenders and much more in the real estate sector. Landmark
UK (a subsidiary of DMGi) identifies and translates environmental and property
risks into facts, insights and opportunities.

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the players for this. It is a wonderful and a great initiative that Liases Foras
has
done.","imageSrc":"data:image/png;base64,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"},{"id":4,"name":"Niranjan
Hiranandani","designation":"Managing Director","companyName":"Hiranandani
Constructions","description":"The integrity maintained by keeping away from
broking and selling is what puts Liases Foras in a privileged position in being
able to give valuation and
advisory.","imageSrc":"data:image/png;base64,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"},{"id":14,"name":"Gautam
Chatterjee IAS","designation":"Former
Chairman","companyName":"MahaRERA","description":"The prospective buyer today
has to depend on the expert opinion of the agent or the developer concerned or a
well-wisher maybe. Property science will not only bridge that gap but also bring
the much-needed objectivity in this regard. This portal shall bring transparency
in pricing and ability to make informed choices to a different level
altogether.","imageSrc":"data:image/png;base64,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"},{"id":15,"name":"Gautam
Sanyal","designation":"Head Retail Operations Risk and Credit
Monitoring","companyName":"ICICI Bank","description":"Delay by a couple of days
in physical valuation is eliminated with Property science. Customers will get a
better turnaround time and an excellent experience. It also eliminates the
inconsistences.","imageSrc":"data:image/png;base64,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"},{"id":16,"name":"Imtiaz
Ahmed","designation":"Executive Vice President and Business
Head","companyName":"AXIS Bank","description":"Property Science is a value add
for lending institutions. Today\u0027s data is not standardized. There are a lot
of subjective approaches in the valuation industry. This can be one step towards
standardization. There is going to be a huge amount of operational
efficiency.","imageSrc":"data:image/png;base64,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"},{"id":13,"name":"Sanjay
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sources and you\u0027re one of them and I\u0027m sure this platform is going to
help the entire
industry.","imageSrc":"data:image/png;base64,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"},{"id":3,"name":"Sucheta
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Editor","companyName":"Moneylife","description":"Pankaj Kapoor is undoubtedly
the best and most honest property researcher in
India","imageSrc":"data:image/png;base64,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"},{"id":5,"name":"Suman
Mishra","designation":"Senior Vice President, Strategy","companyName":"Mahindra
Group","description":"We worked with Pankaj on a project and were impressed by
the high degree of knowledge, insights, and professionalism that he and his team
bring to fore. It was a real pleasure to work with him and would highly
recommend Liases Foras and Pankaj
Kapoor.","imageSrc":"data:image/png;base64,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"},{"id":8,"name":"Sriram
Kalyanaraman","designation":"Managing Director and Chief
Executive","companyName":"National Housing Bank","description":"We are working
on a complete revamp of Residex and we are likely to come up with revamped
Residex over next three years’ horizon. NHB is also working on two more indices
which include rental index and building and construction material index. We have
roped in the realty research firm Liases Foras for providing us
inputs.","imageSrc":"data:image/png;base64,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"}]}
Testimonial

RENU SUD KARNARD

HDFC , MANAGING DIRECTOR

The future is certainly hugely digital and I think Property Science is going to
go a long way in helping all the players for this. It is a wonderful and a great
initiative that Liases Foras has done.

NIRANJAN HIRANANDANI

HIRANANDANI CONSTRUCTIONS , MANAGING DIRECTOR

The integrity maintained by keeping away from broking and selling is what puts
Liases Foras in a privileged position in being able to give valuation and
advisory.

GAUTAM CHATTERJEE IAS

MAHARERA , FORMER CHAIRMAN

The prospective buyer today has to depend on the expert opinion of the agent or
the developer concerned or a well-wisher maybe. Property science will not only
bridge that gap but also bring the much-needed objectivity in this regard. This
portal shall bring transparency in pricing and ability to make informed choices
to a different level altogether.

GAUTAM SANYAL

ICICI BANK , HEAD RETAIL OPERATIONS RISK AND CREDIT MONITORING

Delay by a couple of days in physical valuation is eliminated with Property
science. Customers will get a better turnaround time and an excellent
experience. It also eliminates the inconsistences.

IMTIAZ AHMED

AXIS BANK , EXECUTIVE VICE PRESIDENT AND BUSINESS HEAD

Property Science is a value add for lending institutions. Today's data is not
standardized. There are a lot of subjective approaches in the valuation
industry. This can be one step towards standardization. There is going to be a
huge amount of operational efficiency.

SANJAY JOSHI

HDFC , ADDITIONAL SENIOR GENERAL MANAGER AND REGIONAL BUSINESS HEAD

There are very few reliable data sources and you're one of them and I'm sure
this platform is going to help the entire industry.

SUCHETA DALAL

MONEYLIFE , MANAGING EDITOR

Pankaj Kapoor is undoubtedly the best and most honest property researcher in
India

SUMAN MISHRA

MAHINDRA GROUP , SENIOR VICE PRESIDENT, STRATEGY

We worked with Pankaj on a project and were impressed by the high degree of
knowledge, insights, and professionalism that he and his team bring to fore. It
was a real pleasure to work with him and would highly recommend Liases Foras and
Pankaj Kapoor.

SRIRAM KALYANARAMAN

NATIONAL HOUSING BANK , MANAGING DIRECTOR AND CHIEF EXECUTIVE

We are working on a complete revamp of Residex and we are likely to come up with
revamped Residex over next three years’ horizon. NHB is also working on two more
indices which include rental index and building and construction material index.
We have roped in the realty research firm Liases Foras for providing us inputs.

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Liases Foras, founded in 1998, is the only non brokerage real estate research
company in India. Data and science form the core of our services, which range
from providing market intelligence and risk advisory to lenders and mortgage
companies, to providing development advice, best use, and valuations to
developers, funds, banks and corporations.


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 * Maintain Archive of Valuation Report
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 * Premium
 * INR 499 per valuation
 * Buy Now
 * Valuation of apartment, independent house, or a plot of land
 * Data of Comparable
 * Guideline Values
 * Market Dynamics
 * Price Correction Risk
 * Valuation Report in PDF
 * Maintain Archive of Valuation Report

 * 
 * 
 * View Sample Report

 * Note plus GST as applicable

 * * Please agree and accept the terms and conditions before you pay

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