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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
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. 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.
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.
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.
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50 Users team
Shared Workspace
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Do institutions lack the correct address or coordinates for properties?
Created a machine learning algorithm to geocode property addresses based only on address text.
How to identify the top 3-5 comparable properties from millions in the database?
Created a recommendation engine and ranking to show comparable in descending order of relevance.
Does the actual price vary due to building and neighborhood quality?
Created a proprietary model of price prediction based on “Distance, Density and Surrounding”.
Do institutions have insufficient data to assess price correction risk?
Created a model to assess the price correction risk and productive value of the property.
What’s the difference between market and appraised value?
Created a unified data structure which enables seamless integration of guideline values of different states in India.
Do some institutions use guideline value for mortgage processing?
We provide quarterly and annual trends of primary market of supply, demand and price along with location ratings.
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