Having started my career as quantitative analyst i've always had a data first approach in any of my business decisions, often as a validation mechanism but sometimes as a decision maker mechanism.
While Financial industry in its totality has embraced Big Data and AI in their investments, Real Estate is still way behind in the Data Analytics adoption, at least in full scale.
This lag is also ben due to the inherent relative scarcity of data due to the lack of digital real estate solutions in the industry.
But the Covid situation accelerated dramatically the digital adoption within the real estate value chain, and the various lockdowns increased the en-masse adoption of technologies to work remotely.
With this shift to smart working solutions (from tools to work remotely to tools/services/technologies to remain in contact with business partners, customers, providers and different stakeholders), needs of digital alternatives increased, increasing adoption of digital solutions (digital signing, platform to manage the transaction process, to tour properties and make due diligence, reservation platforms..).
The fundamental point is that where there's a digital solutions there's also a digital data. And this can be extended to the smart solutions installed in assets, where IoT can provide ways to manage efficiently.
The more digital adoption, the more data generation and more density and quality of data: this is the virtuous cycle that i've underlined in my title.
The industry wide transition to digital process and platforms will only deepen the pool of real estate data and with it the investment into data and analytics Proptech companies. With the explosion of IoT in real estate, the way that data is captured is increasing everyday. Whether it's through sensors, cell phones, biometric readers, access control, cameras, or back office management systems, it is hard to comprehend how much data is being collected in the real estate industry. There are plenty of privacy concerns that tenants and visitors have when it comes to data, but the value that it can bring to both landlords and tenants is enormous. The art is in figuring out what to do with it. Whether you are trying to monetize your data or figure out how to use it to create value, it is the companies that are most creative that will figure out how to use it most effectively.
More skills and trained personnel will be needed to manage and use data within the organziations and extract value from them to be provided to the management acting to improve efficency. At the same time more effort from government bodies to open their database and foster data standards will be essential to share and interact with larger datasets.
The necessity to adopt data standards across the industry is a priority: too much operations are operated as silos and too much money spent in legacy software unable to manage the entire value chain of an assets and unable to integrate big data and visual tools.
In the real estate industry there is a pressing importance to understand the business value of data generated in buying/selling, renting or managing real estate, facilitated by PropTech 3.0 companies able to provide disruption.
In the real estate investment management there's a small adoption of a data strategy at broad based level, able to integrate, structure, standardise and digitalize their existing data and leverage portfolio decisions. It's the essential framework to extract value from new and different sources of data, with new technologies and from different channels.
Imagine how a cloud based platform, centralizing large data from assets owned and operated, could improve deal closing, cost monitoring, data sharing and better manage tenants relationships, more again in a model where operational real estate will be the driver to differentiate innovative real estate players.
I'm thinking for example to non pure-numerical KPI related to community building, tenants satisfactions, emotional or soft values, smart indicators that emerge with the adoption of ESG criteria also at board level, translating in financial impacts.
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