Covid-19 may well be re-writing the way to make money in real estate. Picking a promising location, borrowing a large amount of money, constructing a building and then collecting rent and service charges while the capital value of the investment increases is not a prudent methodology when a viral pandemic is putting the whole idea of commuting to a downtown office at risk. The real estate industry needs a new model that can help investors understand the present to better predict the future, because what worked in the past is no longer a reliable guide to what might happen next.

Business, like life, is understood backwards but lived forwards. Decisions are made in a present receding remorselessly into the past, where systematic recollection turns them into knowledge. This is why we reach instinctively to the past for guidance about how to make decisions that will affect our future. 

Magazines and newspapers are replete with the lessons history can teach us about plagues and their economic and commercial consequences. Yet the present is not just receding into the past. It is always advancing into the future as well. What if we could capture the present in the process of becoming the future?

This is exactly the question Sam Hocking, managing partner at San Francisco-based PropTech SMH Analytics, is asking about the real estate market. “How can we better understand and predict what is happening to the real estate market right now?” he asks. “To do that, we need to understand how human and economic behaviour is affecting the propensity to go back to working in offices, what that going-back-to-work process might look like, what it will mean for rents now and in the future, and whether companies are having such success with remote working that they are beginning to think that a big city office is no longer essential. We can learn something from history, but what we really to need to know is what is happening in all these areas now, in real-time.”

At the moment, the only information real estate owners and agents have is anecdotal and backward-looking. Like everybody else, they can see that business districts look like ghost towns. They too have heard from tenants that remote working is proving so successful that they are wondering if they will need quite so much space in mid-town Manhattan at $100 a square foot or a large building in Canary Wharf that costs them £70 a square foot. But agents counter that people go to the office for the conversation, the camaraderie and the intellectual stimulation, and that a grisly commute is a price worth paying for these all-too-human benefits.

The tension between these two views will eventually resolve itself. But that might take years, and in the here-and-now landlords are facing demands for rent reductions and investors are pondering whether to back new office building projects. If Covid 19 precautions mean that only one employee can occupy a space that previously was filled by two or even ten, the lease starts to look a lot less attractive. Even in the short term, office life cannot return to normal when public transport is limited, arrival times are staggered, access to buildings is controlled, two metre auras are imposed, one-way circulation plans are applied, and there are no restaurants or bars or kitchens or canteens but plenty of tiresome and time-consuming hygiene routines.

Disruption of this kind has massive implications for rental revenues now and in the future, and for medium and long-term investment plans in a highly leveraged industry where it costs more than $1 billion to build a skyscraper. It has equally large implications for individual buildings, since every office space in every location has its own blend of location, transport infrastructure, facilities and tenants, each of which is affected by Covid 19 in a unique way. A rent cut for one tenant in one building in one city, which later proves unnecessary, might easily have disastrous knock-on effects on other clients in other cities. A decision not to proceed with a project might later mean exclusion from a boom town. Better information means better informed decisions – and that information is specific, not general.

“Each landlord or portfolio manager needs to build a real-time, iterative understanding of what is going on with their particular business, cities, properties and clients so they can understand what is happening all the time throughout this crisis,” explains Hocking. “They need a dashboard which updates in real-time, giving them a dynamic view of their entire property portfolio, based on debt-to-value ratios, debt maturities, types of lender, the financial strength of the tenants and rent rolls, expiration schedules, locations, transport infrastructures and other data. The dashboard would assign a health score to each individual property and aggregate the findings for the whole portfolio.” The question is how to go about building such a dashboard.

Hocking reckons he knows how. Having founded, built, ran and sold AltX, a data-driven matching agency for investment managers and investors, Hocking has worked at the interface between data, advanced digital technology and investment and investment financing since 2012. AltX solved a distribution problem in asset management by making use of artificial intelligence (AI) to process formidable amounts of structured and unstructured data. Hocking reckons the same technique can now be applied to predicting what will happen in real estate markets as the global pandemic plays out. “This is about understanding what tenants, lenders and landlords are thinking and doing right now,” he says. “This is what we call `nowcasting,’ as opposed to forecasts based on backward-looking data.”

And what nowcasting entails is bringing together the same sorts of resources that AltX assembled eight years ago. “The way you do this is by choosing a team to design the problem, and identify the streams of data that can solve it, hiring top-notch data engineers and advanced analytics specialists to build to the design, and assembling a visualisation team to design the front-end application – the dashboard,” says Hocking. “We need designers, engineers, data scientists, behaviourists and social scientists and great tools like high-speed computers, a computational analytics environment and a machine learning sandbox and technology platform. Very few firms in the world can do this kind of work, which is why we see an opportunity for a firm with our experience to improve decision-making by people working in real estate.”

That helps explain why SMH Analytics has already received an offer to purchase the business, although it is still at the pilot testing stage. Hocking says the firm is working with a global commercial real estate services firm to help it analyse the impact of Covid 19 on the investors and tenants that it advises. “The client likes what we are doing,” he adds. “He told us that he wished he had had a tool like this in 2008 during the financial crisis and since to see how cities develop, because it is the real estate in and around fast-growing cities that drives investment performance.”

At present, decisions to invest or not to invest in a location are based on business experience, walking around individual properties and cities, and talking to local and national brokers. It means firms often miss attractive opportunities in fast-growing cities because they focus on what is visible rather than on the underlying structural and economic changes that are buried in dozens of apparently unrelated and often unstructured sets of data. 

It is these previously invisible trends that can now be detected through deep learning algorithms and the addition of science and mathematics to the traditional arts of the real estate investor. And they allow investors to see more opportunities to make money. “This pandemic is a time when many people will be working to understand the `new normal’ in real estate, and they will inevitably find it hard to obtain clarity if they rely on traditional methods of evaluation only,” concludes Hocking. “But some will see and seize opportunities, because they are processing more and better data and understand what it is telling them.”

Written by Dominic Hobson - May 2020

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