The Institutional Real Estate Inc Sponsorship brochure, Connected-Investor Focused, We connect people, data and insights, sponsorship, events, IREI Products
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NATIONAL ASSOCIATION OF REAL ESTATE INVESTMENT MANAGERS 24 of physical venues and one-off events in a city to provide a detailed view of arts, nightlife and other leisure activities. This includes the exact location of amenities like bars, cafes, art galleries, music concerts, food festivals and exhibition openings. By utilizing proprietary methods to calculate locational proximity and distribution of such venues or events, we can spatially sort and rank this data across entire cities. The picture above shows, for London, which areas have the greatest number of new bars, cafes and restaurants (in this case, those which have opened during the past two years). However, this view can also be filtered to look at a more specific subset of the data, such as restaurants or coffee shops that are not part of the major chains. The data also allow us to look at less spatially fixed behaviour, such as events that might not take place in a traditional venue; which could be pop-up markets, outdoor cinemas or all-night warehouse raves. In doing so, we can access a view of the city that is rarely seen; and understand which neighbourhoods certain groups of people are likely to go to in order to spend their money and enjoy themselves. We then combine the increase and decrease in an area's popularity with rental price data. Information on relative rental levels in a city in conjunction with the relative increase/decrease in local economic vibrancy provides a "gentrification indicator" that can be used for site selection. Low rents and strong increases in economic activity, that's what developers and investors should be looking for, which is exactly what the Grapvine Dashboard points out. Of course, macroeconomic data may shift some of the institutional focus away from London. But the concept of Grapevine specifically, and the use of big data more generally, can equally be applied in every market for every property type. As the availability of data grows, investment decision making will increasingly be informed through objective analysis. And if it makes you feel better, you can always add a dash of gut feeling. BIG DATA FOR REAL ESTATE VALUATIONS Determining the value of commercial real estate remains elusively hard, with a workforce of 74,000 appraisers in the U.S. alone still manually assessing the value of assets sometimes worth billions of dollars. In addition to the significant increase in the availability of data that the real estate industry has witnessed, there is the advent of machine learning techniques. Such techniques are now widely used in medical research, as well as in applications such as search algorithms and recommendation engines. (Examples of machine learning applications vary widely, from fraud detection by PayPal to the personalized online ads that we have grown used to.) In the single-family housing market, there are some nascent products that use this combination of data abundance and machine-learning modeling, including, for example, House Canary's automated valuation model, and Zillow's much-discussed "Zestimate." We apply "big data" in combination with sophisticated modeling techniques, to develop an automated, machine-based valuation model for the commercial real estate sector. We first focused on the multifamily sector, enabled by access to a dataset of some 54,000 U.S. multifamily assets. This dataset is enriched by a wide set of both standard demographic and economic measures and more modern, "hyperlocal" metrics, such as proximity to music events, bars and restaurants, green space, and local crime incidence. Information on relative rental levels in a city in conjunction with the relative increase/ decrease in local economic vibrancy provides a "gentrification indicator" that can be used for site selection.