Estimation of the life cycle costs of a residential building
The objective of the project is to develop better tools for the administration of residential buildings and to see how the building data can be used for forecasting and planning.
What kind of problem is the project trying to solve?
According to estimates, the amount spent on the renovation of the Finnish housing stock will reach about 10,000 million euros per year. Our buildings are ageing, and the renovations and maintenance are among the most important factors that increase the price of housing. We aim at reducing these costs by developing better tools for the administration of residential buildings. In the project, we will experiment with the use of the data from residential buildings in forecasting and planning.
Objective: To improve the efficiency of administration using data
Our objective is to show how data and modelling can help increase the efficiency of the administration of residential buildings and decrease the cost of housing. At a technical level, we want to show that new and useful information can be extracted from existing data by using modern methods of modelling.
What is done in this project?
We develop methods of modelling the costs of maintenance and renovations of residential buildings by using algorithms. With our partners, we experiment with modelling using data from hundreds of properties and provide information on how forecasting and planning can deliver optimal benefits. The project focuses on basic building information, renovation history, materialised costs, IoT measurements and technical housing managers’ estimates of life cycle costs and need for renovation. We investigate the practicability of different modelling methods, such as machine learning.
After the experiment we will have a better idea of how we can benefit from the digitalised data from the real estate sector. It will give us practical information on the data that is already available for use and whether existing methods of modelling are suitable for the modelling of residential buildings. In collaboration with our partners, we also aim to show that efficient modelling can improve the efficiency of maintenance and reduce costs.
Many of the modelling methods require high amounts of data during the development stage, although a finalised model may function using smaller amounts of data. In collaboration with the actors in the sector, we can develop models and create operational methods that can be applied to any residential building to reduce the cost of housing in the future.