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A property’s valuation is traditionally created by qualified valuers, for instance, RICS (rics.org) is the most recognized international organization of valuers. On the other hand, big data and AI, has opened the world of “instant valuation” based on available market data. Additionally, companies like RCAnalytics (rcanalytics.com) offer access to accurate property valuation data by partnering with large corporations and investors.
Assessment of a property valuation is therefore well established and can be relied upon, although many professionals and RICS members will also tell you that there are cases where valuations made by different valuers with the same tools and qualifications might differ by as much as 10-20%. That being said, banking institutions usually update valuations of their portfolios (properties they have taken as collateral for debt issuance) on a yearly basis, depending on the regional standards of course.
A tokenized property’s valuation, on the other hand, is much more dynamic and can’t solely rely on the basis of a rather static traditional valuation model. For this reason, Blocksquare has developed a valuation model for properties based on the share of the tokenized portion in a property’s capital stack. For example, a property where 10,000 tokens have been issued, the token’s secondary market activity will have a lower impact on valuation, then for a property where 70,000 tokens have been issued.
Defining a safe model to properly value tokenized real estate is important for mitigating risks associated with possibilities of manipulating prices on secondary markets.
With real estate assets, relying solely on token pricing might prove to be challenging as the amount of data sources is very limited and perhaps even insufficient to establish a reliable pricing point. It can therefore be dangerous as a RWA token price could easily be manipulated.
Blocksquare’s protocol mitigates the previously mentioned challenge. Real estate properties hold their real world valuation within the PropToken smart contract itself.