FinTech applications using neural networks:
- Predict housing prices in Norway
- Predict currency pricing
| script | description | |
|---|---|---|
| 1 | model_building_NN | building a neural network (NN) to predict housing prices |
| 2 | currency_pricing | building an NN to predict currency pricing |
The average price per residential property in Norway varies widely across different regions in Norway. Not surprising, the most expensive (mean average price) property is in Oslo, the capital, at approximately 5.9 million Norwegian kroner (as of 2019). The town (above the polar circle) Tromsø was ranked second, with housing prices costing an average nearly 4.2 million Norwegian kroner.
This also evidently, affects rental prices (but we will not discuss this here).
The aim of this project is to provide a benchmark to a real estate agent of how property prices in Norway would be in later years.
Prices of goods in different countries will result in different purchasing power, and is one of the more widely used methods for forecasting exchange rates. The relative economic strength of Norway can be determined by comparing levels of economic growth across countries to forecast exchange rates. While predicting the future direction of a currency is not easy since it is dependent on the mood of investors sensitive to the flood of news, data and other world developments 24 hours a day. However, some predictions can provide information on supply and demand in the markets.
Thus, the aim of this project is to forecast exchange rates and provide information to a savings bank on possible currency pricing.