SVA is a system that was designed to solve the problems of analyzing the value of real estate and obtain the necessary economic indicators online.
We were approached by a customer, major Russian bank, with a complex task of assessing the value of real estate. This is exactly what happened with us when were developing our main product of SVA (Scoring Value Analysis).
Previously, they made many attempts to find a decision, but failed to find a suitable algorithm for solving them.
1) Identification of office classes.
The class shows the level of comfort for employees and is significant issue is price formation: the higher the class, the more value the owner can get for renting and selling real estate.
When looking for an office to create comfortable conditions for their employees, many tenants are guided by the class. The task is compound and includes many subtasks.
2) Determining the level of infrastructure.
For offices above class C infrastructure is important - the availability of gyms, shops, cafes, restaurants, parking. This increases the cost of office. The task is difficult in terms of data distribution - the level of infrastructure does not concern one building, but covers a vast territorial area.
3) Geolocation tasks.
Accessibility of the metro or other transport, location relative to the city center, prestige of the area, general availability of the office. This directly affects the cost and class of the office.
4) The task of finding analogues.
To assess the cost of the office often use the method of comparison with peers. The task of finding analogues is important for determining many parameters, and also provides an opportunity for choice for both appraisers and tenants
5) The task of assessing the cost of renting or buying real estate.
It is difficult to assess the cost of rent without expert opinion, and people who are not directly related to the real estate market prefer to contact specialists for an accurate assessment.
Our comprehensive product solves these problems in stages:
- collects data from many sources,
-conducts a deep mathematical analysis,
- takes into account all the generating factors,
- aggregates data into one database.
Based on complex machine learning algorithms and financial logic, we make estimates and determine the necessary parameters for any type of real estate.