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.

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 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: Distance,Density (Economic Density), Surrounding and Product. A multivariate regression equation that integrates these factors predicts the price of the property for 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 the 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.

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, forming a new setting.

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

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.

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.

This video will showcase a valuation of a premium apartment building — Samudra Mahal in Worli, Mumbai. 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 at contact@liasesforas.com.

Independent house in Aliganj, Lucknow — Liases Foras’ desktop has attempted one of the major challenges of providing the valuation of independent houses, a predominant 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, 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 comparables. 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 comparables.

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

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 can assist in conducting the valuation of premium properties. This video will edify you on how DV can provide valuation of Amitabh Bachchan’s Bungalow – 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 at contact@liasesforas.com.

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 at contact@liasesforas.com.