Determining the cost by analogues
Client:
Real estate agents, investors, developers and car dealers. Details are under the NDA.
Real estate agents, investors, developers and car dealers. Details are under the NDA.
Problem:
A feature of the real estate valuation process is its market nature. This process is not limited to taking into account only the costs of creating or acquiring the property being valued - it is necessary to take into account the totality of market factors and economic features of the property being valued. In addition, the real estate market is very dynamic, so periodic revaluation of properties is required.
A feature of the real estate valuation process is its market nature. This process is not limited to taking into account only the costs of creating or acquiring the property being valued - it is necessary to take into account the totality of market factors and economic features of the property being valued. In addition, the real estate market is very dynamic, so periodic revaluation of properties is required.
Solution:
By training the neural network with data on the objects presented on the market and sold, it is possible to predict their value. This method can provide a more accurate estimate of the cost of apartments and cars compared to traditional methods such as statistics and regression. The neural network can take into account various factors such as location, size, age, condition, and amenities to help make predictions. Such models can be used by real estate agents, investors and car dealers to determine the best buy or sell price.
Creating models based on artificial neural networks for asset valuation can significantly increase the efficiency of organizations.
Neural networks can also be used to determine the cadastral value of real estate. They use a variety of inputs to identify patterns in the real estate market and identify trends in property values. The neural network then processes this data and generates predictions.
By training the neural network with data on the objects presented on the market and sold, it is possible to predict their value. This method can provide a more accurate estimate of the cost of apartments and cars compared to traditional methods such as statistics and regression. The neural network can take into account various factors such as location, size, age, condition, and amenities to help make predictions. Such models can be used by real estate agents, investors and car dealers to determine the best buy or sell price.
Creating models based on artificial neural networks for asset valuation can significantly increase the efficiency of organizations.
Neural networks can also be used to determine the cadastral value of real estate. They use a variety of inputs to identify patterns in the real estate market and identify trends in property values. The neural network then processes this data and generates predictions.
