propertyscience.com
Open in
urlscan Pro
183.87.220.182
Public Scan
Submitted URL: http://liasesforas.juvact.com/click/aqDWJt67wDsPbI6WHN1CUlnQIXD2kwu87H-RoGfJX0NWTQnYQF9snnOsz3-fkWbY
Effective URL: https://propertyscience.com/
Submission: On February 14 via api from CH — Scanned from DE
Effective URL: https://propertyscience.com/
Submission: On February 14 via api from CH — Scanned from DE
Form analysis
4 forms found in the DOM<form class="searcheform ng-pristine ng-valid">
<input type="text" name="txtpropertyaddress" id="txtpropertyaddress" class="form-control ng-pristine ng-untouched ng-valid ng-empty pac-target-input" placeholder="Address" ng-model="UserInput.FULL_PROPERTY_ADDRESS" autocomplete="off">
<button type="button" class="btn btn-form" id="btnSearchSubmit" ng-click="cityaddresssubmit()">Submit</button>
</form>
<form style="padding: 15px;" class="ng-pristine ng-valid">
<input type="text" class="form-control" placeholder="Address">
<button type="button" class="btn btn-form">Submit</button>
</form>
<form role="form" id="contact_form" class="contact-form ng-pristine ng-valid">
<div class="form-row">
<div class="form-group col-md-12">
<input type="text" class="form-control AlphaOnly" name="name" autocomplete="off" id="name" placeholder="Name" required="">
</div>
<div class="form-group col-md-12">
<input type="text" class="form-control AlphaOnly" name="companyname" autocomplete="off" id="companyname" placeholder="Company" required="">
</div>
<div class="form-group col-md-12">
<input type="tel" class="form-control NumberOnly" name="phone" autocomplete="off" id="phone" placeholder="Contact Number" maxlength="10" pattern="^\d{10}$" title="Please enter a valid number (10 digits)" oninvalid="InvalidMsg(this);"
oninput="InvalidMsg(this);" required="">
</div>
<div class="form-group col-md-12">
<input type="email" class="form-control" name="email" autocomplete="off" id="email" placeholder="Email" title="Incorrect email id" oninvalid="InvalidMsgEmail(this);" oninput="InvalidMsgEmail(this);" required="">
</div>
<div class="form-group col-md-12">
<textarea class="form-control textarea" rows="3" name="projectdec" placeholder="Comment"></textarea>
</div>
<div class="col-md-12 text-center">
<input type="submit" class="leran-more btn btn-info btn_style" value="Submit">
</div>
</div>
</form>
POST /
<form action="/" method="post" class="ng-pristine ng-valid"><input name="__RequestVerificationToken" type="hidden" value="3FtRZ_SKFLvpdMfzK9i9uxZs91ROsNDK74Mf7-3Xw9d31R8sIGPhSf8TQi7hCyMGvvK5lUs7Z8jUrYomq2vxlFDoKlXsHHqQTrhlSPR0CMA1"
autocomplete="off">
<div class="login-popup">
<div id="loginFormModal" class="modal">
<div class="modal-content">
<div class="modal-header">
<span class="close" id="loginFormModalClose">×</span>
<h3 class="modelheading">Login</h3>
<span id="loginFormError" class="span-error"></span>
</div>
<div class="modal-body">
<div class="container">
<div class="form-group">
<label for="loginUserName"><b>Username</b></label>
<input data-val="true" data-val-required="The User name field is required." id="loginUserName" name="UserName" placeholder="Enter Username" required="required" type="text" value="">
<span id="loginUserNameError" class="span-error"></span>
</div>
<div class="form-group">
<label for="loginPassword"><b>Password</b></label>
<input data-val="true" data-val-required="The Password field is required." id="loginPassword" name="Password" placeholder="Enter Password" required="required" type="password">
<span id="loginPasswordError" class="span-error"></span>
</div>
<input type="button" class="popup-button" onclick="popupLoginValidate()" value="Login">
</div>
<div class="container" style="background-color:#f1f1f1">
<span class="psw"><a href="#" onclick="showLoginSignup()">Signup</a></span> <span class="psw"><a href="#" onclick="showForgotPassword()">Forgot password?</a></span>
</div>
</div>
</div>
</div>
</div>
<div style="display:none;">
<input class="text-box" id="loginIpAddress" name="IpAddress" placeholder="Ip" type="text" value="192.168.0.2">
<input class="text-box" id="loginCookieAddress" name="CookieAddress" placeholder="Cookie" type="text" value="">
<input class="text-box" id="loginreturnUrl" name="returnUrl" placeholder="returnUrl" type="text" value="~/Search/Index">
</div>
</form>
Text Content
baseUrl [{"userid":"0","username":"Guest","clientid":"0"}] {"DV_CASE_ID":0,"NEAREST_LANDMARK":null,"FULL_PROPERTY_ADDRESS":null,"LAND_AREA":null,"LAND_AREA_UNIT_ID":3,"LAND_AREA_TYPE_ID":1,"CONSTRUCTED_AREA":null,"CONSTRUCTED_AREA_UNIT_ID":3,"CONSTRUCTED_AREA_TYPE_ID":1,"LF_CITY_ID":0,"CREATED_BY":0,"PROPERTY_TYPE_ID":1,"PXVAL_USER":0,"PYVAL_USER":0,"PXVAL_PRED":0,"PYVAL_PRED":0,"COORDINATE_ACCURACY":"Not Sure","PS_VALUATION_REPORT_ID":0,"COORDINATE_SUBMITTED":false,"UNIT_FLAT_NUMBER":null,"IS_PLC_CHARGEABLE":null,"IS_PARKING_CHARGEABLE":null,"NO_OF_PARKING":null,"TOTAL_FLOORS":null,"PROPERTY_LOCATED_FLOOR":null,"POSSETION_DATE":null,"PROPERTY_STATUS":null,"PROPERTY_TYPE":null,"LF_CITY":null,"AGE_OF_PROPERTY":0,"BALCONY_TERRACE_AREA_SQFT":null,"SCR":null,"PRICE_SELECTION_TYPE":"TOP_3","SALEABLE_PRICE_TYPE":"MEDIAN","DV_SECONDARY_ID":0,"REGION_ID":0,"REGION_NAME":"","SELECTED_PRICE":0,"VALUATION_AGREE":null,"QTR_ID":0,"QTR":null,"PAYMENT_GATEWAY":null,"DV_ORDERS_ID":0,"ORDER_ID":null,"ORDER_AMOUNT":0,"ORDER_STATUS":null,"FLATSIZE_SUGGESTION":""} sign in login logout * About us * How it works? * Pricing * FAQs * Contact us * Count 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. {"items":[{"id":18,"name":"Renu Sud Karnard","designation":"Managing Director","companyName":"HDFC","description":"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.","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 Joshi","designation":"Additional Senior General Manager and Regional Business Head","companyName":"HDFC","description":"There are very few reliable data 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 Dalal","designation":"Managing Editor","companyName":"Moneylife","description":"Pankaj Kapoor is undoubtedly the best and most honest property researcher in India","imageSrc":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFIAAABSCAMAAADw8nOpAAACvlBMVEUAAADtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRljtRlj///8AAAD9/f1/f3+VlZWXl5d6enpsbGyqqqqbm5uZmZmQkJB4eHhycnJQUFB8fHyDg4Ojo6NhYWGxsbGSkpKBgYFnZ2ehoaGNjY2IiIhaWlqnp6dLS0tWVlZFRUW2tradnZ0DAwO5ubmtra2lpaWfn5+Li4t1dXVYWFh2dnZeXl5cXFw1NTVpaWlSUlKGhoavr69UVFRISEhCQkL8/Pyzs7NkZGQ7OzuFhYVvb2/Dw8O/v78HBwe9vb0qKiolJSW7u7tNTU03NzcgICCKiooiIiLIyMiRkZFxcXFwcHA9PT0yMjIcHBwZGRkPDw/s7OxjY2NAQEAUFBQKCgruTF729vbj4+PLy8s5OTkuLi4nJyfx8fHZ2dnPz88/Pz/7+/v+8fLW1tbvVWYwMDAsLCz5+fn96uwMDAzu7u7o6Oj95uj4t78eHh4RERHg4ODT09PR0dHFxcUWFhb++Pjd3d3X19f6zdL0iJPvXG3uT2DtR1n84uX4r7fyeYbtRlj+9fbz8/Pl5eX1l6FBQUH83uH719vQ0ND2naf1nKXzgo7rS1zoRlf6+vr60tbyfInye4jwYnF7aGrnV2bgRVa0RFD++frt2tzkzdD5wcf5wMb5v8W9rq+gjI7zf4zYeoWKgIGke4CSeny5dHvPcXvdZXKRTlXZRVV3TVLQQE+aPUdzPkNQOz0tISLl2NnTycrVt7rssrjYqa7OjpXWjJStjpHkhpDYbnnMYGuUZGmVVVzgTFzFTVqnTVe9Q1BdSkypO0daQ0YutcspAAAAKXRSTlMADHhHUEwC6qidh4J10XP3u7eblY9eFwrd2Ht61b9cMhMP8G5rNCp5NVZP+ZYAAA5ZSURBVFjDpZkFWyNXFIapu7u7zDCajGQycXf3hLgnuLtDy8KWpYW1duvu7u7u7t7+i95QSUKof0AeJPNy7hy9d5r+Tsfvc84xR550wBE33njEAaeedtDe+x3f9H+0177HHrblhluvv/K6i5+AoMsvvu6By+56aMthx+57wn/j7b/Pgde8cP3tUINuv/6F0QP32f/fG3jotQ89czn0J7r8mRuuPXSvfwU865Brbr0U+ktdeus1h5z1z4l7j951MfS3uvju0b3/IXDfI6+6BPpHuuSqo/b9J8SDr74SqtMt7RZjB4UZJrY3N0CvvPrgvwWef8w9N0E12pqSObUe7WJ5dSmubs3OQDVqrqz+nmPO/2vicaPXV68Al5g79SW9Wq72l3L5xdKiJ2GYqfn7utXXj57+V8RzttxRa8QM0RPx+xyID7fH5r1D2iGtXsvN1pkJ9MCWc/+cePLV10E12u5cjegcMTXBOOShyFC3Vq0u3tbDtkMtVSQwteW6qy/4U+LDdZ6+onM17Bz0aP1Ox+BQt37SG1bz7qK3vxwFrAps/bMFfFzy8CmbEw+vEsF7t+7oHHLzLkat453u8KQ3PnSblo0UnfNl07AYggCsKuiShw/f1DNbqvnS3DIg8c3rOFmC8Q3SMQ/rXcxNdsf1em93vHcENtmg5ot+s7JCB7m05bhG4nmjd0BVDWB8kZBKKTunQlzucHEyHvH2LHpv809nTCZThVlBQtVAvePeMxqQB10G1ajAquVSsRDxEXhS6y8O6SN+thsEkla/ZoIzJng3CV0EiDWxf9lBDTlzVS1x3DekS6Iit0ft93jULDuvZ9WecDieK8W9fXAGhuHFLLS+8KquOnhDXl99U22EK+KsPyYCC1a7fYQzpJssl7Q6nbbkXZqODOXKu9fgZc8VUL1uurou3/e/tprXzVuhmcHcZNw5GPNEQm4f4naqw7m1fLdeW+pZbFvtiUTYeN/wpGxbvZXQlUfVReRV1YRo2TZjmCxPd3u6Wa2e9ctpvDOkjZdHTMttUzlvqae3v381P7Wa90jqkWDpe9dU3NGaiLx/6wqdb+uL6EdyJW1I2+lKcDFPcSi3u39tOhwK+f3e1RF4Gc6EErNQPfKS0WqhP+SuqpHNY1u3+XZnMrleeHhRx2vlIpGSwxGZg5CrPXJ5p5z3hNze4anVpUCwBarX3Yf80Weuqa3hF120MJ+BM7uH4ZE2nVw3SBMIhqKoQIiCIKUwsUTDBRKZtZzJSW700MXX/G7m3rfWersFipZhODMyDJuWirxOnUQQpdVAKhQ2K2lOpwqkABNKdCCOeqVzG2vyrYf+7u5LasvV/RC2DI8sZ/phuMfvlPNEQhA0p9OGYDAqMZg7Qno2hmOGPAwv0zvGNva4a3/txfs9VCUCtUAIbBoxZTJwf0knRwKoJDWxCxAlEqsV5acXk9IOKmDmYNgkjw5AG3TDPuvIA5+uJzYTcH+mknW9rJNG07MpI9pBCWwKi0AW645wChQlJRZzH2zyyG7ZiHz2wF+dc3kV2QKQW3nTmgkYYeqmcTRrUMiSOq/XiVNBcWdI7eLj3ZOs0HCnGoYjnbsaZobREyq5eA8E1fWTAXakHwbIPsaIBYWUxiIRIzG/mkqnKNbdDWcya/CQIiWE4elAAdqoB/cDyKMvq0OCwlYyZWAgPanRKAOGdjPmktM0L03NWdR9/X0w7AmPMGQ2D5u6djQgrz8WIE+8tI7YDM2Cq4AynMQqUAVJoqe3bziv45XZnXfKvHysLcfjiFIzEYLh8HjjDHYYmB+31BDXP6LwutqsGiumIFnw7W5Gv8RILQuzQVxGxORqQpPatZOG4eFGJLTlzKb9Hqz+2LJupfhXZI9BQypsSbjfm2lT0rEOsWXH+IKYczCdLkrTvnOPdBiG90DNm4TR3nfVWQnekvgVWTJrrAJksS/MFt1uOUJKzDvGxtMWK0oJo7vaDRPWvAmegBp096GgQdQhwav8V6TeohBK6aRNShDyJC4uRNN7ZgYWJIX2gtkgEaBZEvjQ0Ii87Oim0x6AgGq7rRb4VAuPhG2YwBjM2igVgifI9kLUvGNlfCCLpebasxLSGg2ChVONyCuPbDqpbsAAkT5zGwyj1PRyr1Rok0gMBYPFAlIyVRCId96yfXwnKk2l0gZJNBrtzcA0tJnLD7h4g5Xb5mF4Ctctm3xigcQilkii2Ym5VJoS7JnblbWkF1QqSzRKaiTB4WHYt8l0fGLTEfUDOUieOAzS1xXKawWohhRiwmA2lSooCHNKJbVTLCvlKIWGNGqsfUtwaJPZ+ICmG6F65PrC4Wkrqo1zQqNYrACuNkdJUJF64KUk3+clLbJWIYZZyd4c7G9EPnFEIxIiKjVDaQ3rGIC0WkhSItEokaDQX5wv6RFJoWDEkVYMNUzBsANq1I2NC4cEI5VAz/IEJ7UZjVaNRiNGW43pdFBijDGWbDQa5BiRsiMaB0HUGOo3nV1xzwZJ4Iq60jKlUGBEMYVCgUkxMlswZyd2Fczm9l0SFeFQoeZ5uG/rpvcSBNEGrfRWkCZFGjUKbEJMABoZqlRICnemhChGsDwm8BEJVKhZgnGoUdedWg31qgjYBJj8hFJgMWKo0NghVQWMhXbKu3s1v5TPLw3pXBT4T/DaGNS8WaiDhNyoHXD/NGiA7RZp0CaVYjZURXO0OtJXDjlUmF2FGkW4ym50TIHRYLOErJaNag2G/DDSBsPYrNSmwTpQIUbJkvp4TCZUYJhUZiejGCJqtZfmwIy5admoFrdqq5iAI26Q6CuoVCHAUCmK2X2MxSymaF7td3ZRYrJD5NAkDOvbgMbiVi3BNS0NcsGg+4zwTgS4p0OpbE3QUo1N2Joc9BGdOGUlhSp+QNAK1r1pCQaNoh5ZKR274IpMcBslpACRQziBFZQ6BZgRsuagVSwkKCiBVpCNVaPazupu50C4gtS2TSkZu0oVQOiAwCoWm9tT2SyYO8QdtH8XZLcA5ObtDDTdRibUHpV7YwJoRScXIUAgaBRk1BDUiAWoEHWo+QloZaZCbGy61dGgQdvmwngztF3vcYhcLkSlxAQ2o5AK0A48qeaZnX/s9+rAN42eUB1gGi1dyOnHb5aGWKc8WRktXbJAQMTI5byTdRP4HLR1rOU34MD2K7YPNNcMME37PLQ5cg8bv2UB9vu0zsFBtd/vD6ndaqcupAWjW5cyuHIzdNF6EN2SVYD6aSncObAeQvXDYH3HaBlT8NB2r9bhYZ1uNw9CkmWL2mIkrtcN4mKpAZqpFI2b2wWoggwazBLSkhoHw2B1ZG0w8qLKQDwrZ2O+Ljbi1zmdflarjXTHvaWwm7CLMde4eW571oKJRAjXuk4F8+KLhzcM1rXhOTZrMepKfgcO9rm5qXm9Pj4JdpK5qUmWxylMIjNYrFyS8fh9IiRBCTWSqMH83L17/TH+372R1wLtRO0IItJOdiEivCvkLfebMmu7e/NtU/oiz7XaNMZtaZkKVSGIjEtQimjBLLGQrx1SPWcavaSeeHMhLQSJSKH4IONIMnhAJi9OTg/3D/e15XoicgQlhTYDRmFgZiLFNmvQbCBtAuHzVSPB3bwKqtOEzWiTchxCJ+yVzQnY9tEI4hoMFeNeFoweMrRDacfBxiIQsCsrpdOIqVwx9vG6Y6OjrqwN8naS1Ig1YrGAQlx0l8cZisQjkbAuRvjcPONiuhy4CBHJOE7GyRCE47rUbDheKr171Obb0orMNkuFCYZBsQ3l8E432Pl5p8vlXI93XlsMh8NsSBdi/TqdM8QWI0t5b1hb1BEvfbvf5ptnEJD3W8SAaLWKBUIBSEK7iyZ0+rh3qty3e224t5yfzrfl82256d5lU38539btlmEouO1fHfwnW/yxm8euiIotYpsQlYK2A7ZNCjvBdLr1PT0lb0+uvNpW7m3rXYbBPma4x4PbpbagRUC1JuyvHt20UWeM3vFrhM8YzJSAtKFKmYhTYkIM9B6EFjFurb57Htja27880j+tdxKcUGIgjaCL2DkkYH/5vjM2Py65/6KWMWjrbEqs0AikKlGnS4WiUikKWk6HVClLunWR7mIsyQSMFolBYlSqVK2tCQQEbuCVx47b9FDn6ifHFxbm9rSsXLQNU9gwNIA76ASITnCpXYqimAADeDDTkFYM1HqVDOECHFgA7uIeeRtk4mY65dHnUgt3rrTMzq0oEAUotgEXQRAyVSKQUNkralUFAIKzU5SyVYbjCEK7aNrhQB55+5Q/Pc569KnxcejmufZbds5pUNDAAsyg05mUIUAclwhwMhEiC6gCMvAbQMQdDhx3ELJX3qwSG9cOjvGusO5o3rZ9oJ2gNAqj3eHTDbGMqItgHCIABl/gRSTCcVHFPpxmCNFbj9WsulGnj34aTFt2XgHNzKZdPkwhRkWEerLkIXw8L++kabwCEwESA3KIpl0M4frmvtOb/lIXHvTlhzus6eZtY2MTGjGFGo0ge/WLYTfvdjo9cqbiDRft6kp2MUwnQ9MvPX7QeX9/cPvoJ+YB6Io9Y7OQmUCkqK1D1KW7Tc96Yrxa7eHlRJJhQHESuRhwJ79+8+B/dLx81D1PLoyb98xIEryPL+IdRhXtc2qHWD4GWhA4OQJQ4CbE4fvxp6P2+8eH4C9+kJoYKCjtjG4+lIzZhSoREWnLR3TumFwu9zEyu5LWvffOfXv/i7P/Q0a/eGpsIRvECEbkmRoa5MTChJyNhJ38IDjYQWRdzp/fue+Qvf7tA4UbPi6YURWJMri2XJ4nUKugFdRhHx8Kh/Xv/nDjoXv9l8ce977+2UdWZauqQ6Yf6ZvSg7voI7o6H3nj8fsqjz3+k07Y79jD7n39tVdf/vz5R7qX3n//vbfe+P67x046Bjyc+T86c59zjznysBPBM6SzDzj1yKMP3edvHyH9AvS4tq3o4gtRAAAAAElFTkSuQmCC"},{"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. prev next OUR CLIENTS prev next OUR MEDIA PRESENCE prev next * 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. CONNECT US * ADDRESS : Reg office: S-6,Pinnacle Business Park, Mahakali Caves Road, Near Ahura Center, MIDC Andheri East, Mumbai- 400093 * EMAIL : contact@liasesforas.com * CONTACT : +91 8879643976 * HOME * ABOUT US * HOW IT WORKS? * PRICING * FAQs * CONTACT US © 2022 PROPERTY SCIENCE. All rights reserved Terms and Conditions × PRICING Basic Free * Valuation of apartment, independent house, or a plot of land * Maintain Archive of Valuation Report Continue Premium INR 499 per valuation * 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 Buy Now View Sample Report Note: plus GST as applicable Please agree and accept the terms and conditions before you pay * * Basic * Free * Continue * Valuation of apartment, independent house, or a plot of land * Maintain Archive of Valuation Report * * * 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 Buy Now Test Mode × SIGNUP Choose the profile that describes you Choose your option Buyer Seller Broker Valuer Bank HFC Others I agree to terms and conditions and privacy policy I would like to receive marketing and promotional material from Liases Foras Login Forgot password? × LOGIN Username Password Signup Forgot password? × FORGOT PASSWORD Username Login 612 Shares Share Share Share Email