Interview With Timo Valsi On How SkenarioLabs Predicts Property Renovation Demands With AI

Interview With Timo Valsi On How SkenarioLabs Predicts Property Renovation Demands With AI

We asked a few questions to our CSO, Timo Valsi to get a better understanding of how SkenarioLabs forecasts property renovations and its costs. Moreover, we looked into how automated analytics on property renovation demands can benefit asset managers, investors and property developers.

Can you please explain briefly how SkenarioLabs forecasts the upcoming property renovations?

We forecast future renovation demands for every major building part and technical system within the property. To reach the forecast we have mapped all the most common building parts and components by building purpose type, era and building materials and technical systems. Using these building characteristics and known actual maintenance and renovation history in properties together with some other parameters has allowed us to develop a model which assess the technical status and future renovation demands of properties. We have building renovation data through our data partners who allow us to train our machine learning model with their data.

How can Asset Managers benefit from SkenarioLabs service?

Recognizing future renovations or previously neglected renovation needs has definitely an effect on the potential profitability of the portfolio.

We cannot and do not necessarily intend to compete with technical due diligence. However, we can and we do raise flags to increase future maintenance, repair and renovation costs or risky structures or systems. When your portfolio consists of dozens of properties – or more – it is not feasible to have expensive in-situ reviews all the time for all the properties. You need to extrapolate and concentrate your resources to the properties that have the biggest defects or potential risks. We can help asset managers to save their time to focus on the actual problems.

Together with the valuation side, we can also dissect the effect of the technical value on the market value of properties. This allows asset managers to have better portfolio management and investment strategies.

Are there any other types of industries that would benefit from SkenarioLabs? If yes, which ones and why?

Of course, property investors can use our service also to prospect possible investments and to screen the costs risks related to those. Also, those providing building management services or renovation services can benefit from our methodology.

What are your biggest challenges?

In deciphering building renovation history data we have a classification problem because the existing building data is of varying formats and quality. For example, maintenance and renovation history data are often in free-text form and quite vaguely expressed. Therefore we need to have good algorithms for word and expression recognition.

Building part life cycles have relatively large variations which make estimating their remaining service lives quite demanding. Additionally, the data on part renovation history is biased: we need to acknowledge that previous renovations have been made with different portfolio strategies in mind – and sometimes without any strategy. It usually requires some maintenance history data in order to tell e.g. whether a renovation was done pre-emptively, on time or too late.

What’s there for you in the future?

The work continues indefinitely. There is always something new to add or something old to improve. Currently, we are working on a data model for renovations’ dependency on operational and maintenance costs and additionally plan to implement optimization for renovations synergies and lost profits due to renovations.

If you would like to get a demo about this topic or simply ask questions, you can contact directly Timo Valsi at timo@skenariolabs.com or contact us below.

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