Over the last couple of years, a lot of new rules have been implemented in the financial sector due to Basel II and III. One of these rules is that when financial institutions – banks or mortgage lenders – have a loan where there is real estate as collateral, the property should be valued every year.
This is to secure that the value of the collateral still is higher than the loan.
This valuation has been done manually and in most cases by a real estate broker. The real estate broker is visiting the property and makes a valuation report. The time spent is approx half a day and the cost is 1 000 – 2 000 EUR per report.
Because of the time and the cost, properties are only valued when the loan is given and perhaps after 5 years. But after Basel II and III have been implemented, the banks must do the valuation every year.
Therefore, there is a huge market for automated/machine valuation.
The valuation model should be based on the cash flow model/discounted cash flow.
What is the income every year over the next 10 years, and what are the running costs?
To build this model it is important to know the rent level in a certain area.
• First problem is to calculate/predict what the rent will be for the building with an exact address. • Second problem is to decide what factor the cash flow should be discounted with and what the risk premium is for a property investment with an exact address.
Available data to calculate these two issues:
• Properties for rent all over the country for the last 5 years.
• All vacant locations in the country, so the vacancy rate can be calculated and hereafter calculating the risk premium.
• All property transactions back to 1965 with prices.
• A database of all commercial properties in Denmark.
If you and your team have the idea and the resources to solve these two problems with traditional statistical models, machine learning, or other advanced statistical methods, we will be happy to discuss your options and terms of collaboration.
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