Predictive Analytics

New tools are boosting the power of predictive analytics.

Knowing the future of life and death for everyone may be the ultimate goal of predictive analytics. However, first it's essential to determine how it applies to specific industries such as commercial real estate.

Commercial real estate professionals apply predictive analytics using the classic CCIM demand/supply/gap model. Combining powerful, cost-effective computer capability with big data sources provides a greatly expanded version of the CCIM model in a business environment fixated on instant gratification.

Traditionally, the real estate model concentrated on population growth, job growth, and household formation, which predicts demand for industrial, residential, retail, and office property growth. The same technology driving the success of Amazon also gives commercial real estate professionals new tools to evaluate demand and analyze supply for more accurate analytics.

Ascendance of 24-Hour Cities

The commercial real estate economist Hugh F. Kelly recently described how an office rent premium of 28.8 percent in the 24-hour cities of Boston, Chicago, New York City, Philadelphia, San Francisco, Seattle, and Washington, D.C., compared to their adjacent suburbs during a 21-year study period. The definition of a 24-hour city is a place where people live, work, and recreate, according to Baruch College research.

Also of note, these same 24-hour cities are included among the top 20 contenders that Amazon recently announced for its second headquarters. This presents an unusual opportunity for the use of predictive analytics.

The Amazon request for proposal describes the city characteristics, which closely parallels the content of the CCIM  CI 102 Market Analysis course and is based on the market-verified principle that market analysis drives financial analysis.

This condensed predictive model format uses Amazon criteria applied to the cities of Indianapolis; Pittsburgh; Columbus, Ohio; and Raleigh, N.C., to analyze the commercial real estate investment. This assumes none of these cities will become Amazon HQ2.

CCIM Institute offers members a packaged set of predictive analytic elements in STDB business analytics and optimization. While the analytics can be sophisticated, the simple basics also can be valuable. For example, the demand model from the CCIM CI 102 course is one of these basic, but very powerful, models.

Applying Predictive Analytics

For the application of predictive analytics, the focus will be on total employment, as well as educational attainment defined as the number of workers with four-year college degrees and those with advanced degrees. The education level of the workforce and employment-to-population ratio indicates the ability of a local economy to create jobs.

MJ18_21a

A higher employment-to-population ratio will have a positive impact on the gross domestic product per capita. The growth industries in the U.S. economy include intellectual property, artificial intelligence, and other knowledge-based sectors. In these sectors, companies will move to or form start-ups in the cities.

The graphic illustrates the employment and educational levels for the four selected cities. Designated market areas were used to define the study area, and nonfarm employment was used to calculate the employment-to-population ratio.

Columbus, Ohio, ranks highest for the ratio score, but Raleigh-Durham, N.C., has the most educated population. Other more extensive and rigorous analytics include population growth predictions; jobs analysis; and long-term occupancy by product type and market class combined with rental rate and sale price history. Evaluating these criteria deepens knowledge of the market to devise more detailed and accurate predictions.

Toronto also ranks in the top 20 cities for Amazon HQ2. Its unique attribute is an 800-acre area on the downtown lakefront called Quayside. A Google-owned company named Sidewalk Labs has won the contract to remake Toronto as the city of tomorrow. This transformation will include self-driving buses, modular homes, and offices, retail, and cultural spaces all  connected by sensors and other leading technologies.

These analytics are integral components of the CCIM curriculum and STDB BAO. The commercial real estate market is competitive, and time is the scarcest resource. Choose wisely and use CCIM technology resources to be successful.

Thomas C. Bothen, CCIM

Thomas C. Bothen, CCIM, is a CCIM Institute senior instructor and director of investment analytics at One Chicago Realty in Oak Brook, Ill. Contact him at tcbothen@aol.com.

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CIRE May/June 2018