and identify trends and sentiment
before your competitors?
These new data sets needed to create the opportunity for a more real-time analysis of the market, to reach and identify new upcoming areas that would be ripe for investment. So, we began by presenting data from our forecasting charts which demonstrated predictive analysis in areas that showed the greatest potential for growth based on a variety of factors.
By listening to the data, we began to identify growth signals well beyond those of the typical market model, such as economic indicators around rents, taxes, or special incentives to move into an area. An investor after all is primarily interested in the likelihood of recognising a healthy return with minimal risk, which means an informed decision is critical to that success. So, if AI can help to identify opportunities quicker and with greater context and clarity, then the traditional real estate model simply can’t keep pace.
RAS can confidently take proposals to their investors because their data sets can even look at areas that may be considered in decline and spot an opportunity or angle that nobody else has seen. They can analyze granular factors such as the movement and activity in an area, the number of car parking spaces occupied, or scraping social media.
True AI is the ultimate representation of human endeavour which simply means that it needs to be driven for (and by) people who have an appetite to disrupt markets and explore the art of the possible – this entrepreneurial spirit personifies the family at RAS Real Estate.
RAS’ decision to employ an AI model empowers them to take proactive, differentiated proposals to their investors, supported by credible and contextually relevant data to back up their suggested strategy.
Today RAS has a truly unique value proposition and represent an attractive and formidable partner to investors amidst a ferociously competitive market, simply by having the foresight to employ forward-thinking AI. As a consequence, RAS has been able to raise and expand a new and larger fund, expand into a different asset class beyond multi-family properties, whilst also expanding into a broader geographic market and different tranche of investors.
offer than simply being an
interchangeable substitute vendor?
To this end, they built a proprietary automated underwriting and pricing engine that identifies inefficiencies within the residential mortgage space which present opportunities for investors. However, the engine requires hundreds of thousands of documents to be converted into structured datasets and mined before they become useful.
To be better, faster, and more efficient in this process, Rocktop looked to leverage Machine Learning (ML) tools to build a system that could parse the imaged PDF loan files and create a structured dataset that could then be run through their proprietary engine. After two years of unsuccessful efforts to build such a system, Rocktop turned to Altada for a solution.
What if you felt you had more to offer than simply being an interchangeable substitute vendor?
What if the first answer you gave to a roomful of Senior Executives was ‘No,’ because you saw an opportunity for a more important partnership?
Welcome to Altada Co-Founder Allan’s first meeting with Rocktop.
They agreed to a challenge on the spot: Rocktop would supply a set of sample documents, and Altada would build an AI solution to meet the data requirements of Rocktop’s engine.
Over the next two hours, Allan and Rocktop partnered to define the model’s milestones; the Altada team built a prototype on the fly based on those specifications; Altada’s AI scrubbed, tagged and categorized a pool of residential mortgage loans rife with document deficiencies; and ultimately produced a dataset that could easily be fed into Rocktop’s systems.
A first meeting that could have gone nowhere instead became the start of a resilient and formidable partnership
In the future, Rocktop envisions a deep partnership in which Altada's platform drives deal sourcing, underwriting and due diligence, asset management decisioning and execution, and the automation of legal services and surveillance across the full spectrum of fixed income asset classes. And it all started with a simple ‘no’.