Prospects for Improving Real Estate Cycle Conditions for Memphis Area Commercial Real Estate

By Richard D. Evans, PhD.

Emeritus Professor of Economics and Real Estate, University of Memphis Sparks Bureau of Business and Economic Research, Director of Real Estate Research and Director of Revenue Forecasting

Glenn Mueller’s Real Estate Cycle Monitor rates real estate cycle conditions for five Memphis commercial property types. Quarterly cycle reports, covering more than fifty major markets for five property types, are free‒with the latest issue emailed November 27, 2017. [Real Estate Cycle Monitor. Black Creek Group,]

Third quarter of 2017 Memphis industrial markets were in one of the strongest cycle points (CP 11), moving from CP 10, where its Expansion phase was already well along. Brief cycle points definitions are below. Apartment markets in Memphis moved from last quarter’s CP 11 to CP 12, the first step into the Hypersupply phase. Memphis hotel markets and the city-wide office markets both stayed at CP 7, very early in the Expansion phase, while retail moved from one strong cycle point to a stronger one (CP 8 to CP 9).

Very brief descriptions of Glenn Mueller’s real estate cycle model are below, but more complete definitions and explanations are available in his 1996 journal article and his free quarterly report on current cycle point status for more than fifty city markets. [Mueller, G.R., “Understanding Real Estate’s Physical and Financial Market Cycles,” Real Estate Finance, (1996) 12:1, 47-52.] Teams of real estate researchers have developed a method for forecasting real estate cycle changes over future quarters, without needing elaborate data sets on supply and demand variables or econometric software and expertise. A section below makes forecasts for Memphis commercial real estate markets. Finally, a section below suggests how property tax incentives for attracting and keeping businesses may be improved over parts of the real estate cycle.

A Popular Real Estate Cycle Model

Briefly, the sixteen cycle points in the model are anchored by four cycle points that are transition states across cycle phases. A city market is in Cycle Point 1 if it is in transition from Recession to Recovery, where CP 1 occupancy rates are at historical lows for the particular city. Rental growth is negative or close to zero. It is the trough of recession, but is distinguished from other recession phase cycle points in that ill-timed new construction no longer adds to the market supply. Recovery CPs 2, 3, 4 and 5 also generally have no new construction because the city markets improving occupancy rates and rent growth prospects still fail financial feasibility tests of debt and equity investors. Demand growth is bringing recovery. Recession CPs 15 and 16 also fail feasibility tests, but developers committed to the new construction in earlier, more attractive cycle environments.

A city market that is in CP 6 is transitioning from Recovery to Expansion. Occupancy rates are near the particular city’s long-term average. Rent growth is better because demand has grown while no new construction is adding to supply. Rent levels are still too weak to meet new construction feasibility tests. Demand growth stimulates a higher occupancy rate and much better rent growth in expansion CP 7, but rent levels are not high enough to meet feasibility tests until CP 8. Developers see CP 8 as one of the most important milestones, the first part of expansion that will bring debt and equity investors supporting new construction. Expansion CPs 9 and 10 give some of the strongest cycle environments, with demand growth that is stronger than supply growth. Rents are high and growing, as is the occupancy rate.

A city market that is in CP 11 has high rent levels and occupancy rates that match record highs for the particular city. Supply and demand are growing together in equilibrium. Profitability is high. However, CP 11 is a transition between Expansion and Hypersupply. Stays in CP 11 are sometimes long, but since new construction delivered in any period may have spent months or years in the pipeline, it frequently occurs that more new construction adds to supply in a given quarter than demand growth will support--Hypersupply. On the other hand, in some of these periods, demand will grow more strongly than supply. Mueller’s cycle model does not move “forward” smoothly‒it often “stays” multiple quarters in given cycle points, or “go backward” when demand fluctuates upward while supply grows more slowly. Hypersupply’s CPs 12 and 13 still have above average occupancy rates. Rent levels are high, but growth rates are slower.

A city market that is in CP 14 is at a transition between Hypersupply and Recession. Occupancy rates decline enough to be at or near historic averages. With the increase in occupancy, rental growth slows to less than general inflation, and it may show negative growth. Recession CPs 15 and 16 show supply growth from construction completions in difficult times, but rental growth is negative or close to zero. The trough of the recession is CP 1.

Forecasts for Five Memphis Commercial Real Estate Cycle Points

Real estate cycle forecasts of exactly what will happen in the future would be very desirable‒imagine being at a casino with someone who could know what would happen on four future rolls of the dice. Individual commercial real estate market participants have little control over important supply and demand variables. Once committed to a market, they are not completely different from gamblers‒they must deal with only having estimates of the probability of alternative outcomes, in this case, cycle points evolving in their city across future quarters. The forecasting model used here requires 1. knowledge of current cycle conditions and 2. estimates of quarter-to-quarter “Markov chain transition probabilities.”

For Memphis office markets, we use knowledge of a CP 7 status in the third quarter of 2017 and published transition probabilities. [Evans, R.D., and Mueller, A.G., “Forecasting Real Estate Cycle Risks in Portfolios of Office Properties Across Cities,” Journal of Real Estate Portfolio Management, (2016) 22:2, 199-215.] For the next quarter, the highest probabilities are 0.61 for CP 7, 0.25 for CP 8 (feasible new construction), 0.04 for CP 9, but with backward moves of 0.06 for CP 6 and 0.03 for CP 5.

More steps-ahead-forecasts are easy spreadsheet calculations. Using them, an easy calculation gives the expected relative frequencies across cycle points for spans of time, such as one, two, three and four quarters ahead. The expected relative frequencies over a four-quarter span are 37% for CP 7, 26% for CP 8, and 15% for CP 9, but also 8% for CP 6 and 4% for CP 5. Again referring to the casino dice game, 7 is the most likely roll, but all outcomes are possible.

Knowing Memphis industrial market’s cycle conditions in the third quarter of 2017 (CP 11) and using published industrial market transition probabilities, we can calculate probabilities for the future. [Evans, R.D., and Mueller, A.G. “Industrial Real Estate Cycles: Markov Chain Applications,” Journal of Real Estate Portfolio Management, (2016) 22:1, 75-90.] The probability is 0.69 for a stay in those prime CP 11 cycle conditions in the fourth quarter 2017. Hypersupply’s CP 12 comes with a 0.23 probability, while “backsteps” into Expansion’s CP 10 and CP 9 have probabilities that sum to 0.06. These are all profitable cycle conditions. Operating over the span of the next four quarters, 49% is the expected relative frequency of CP 11, while 11% is the sum for late Expansion cycle points. Expected relative frequencies are notable for Hypersuppy (CP 12 at 25%, CP 13 at 8%), and the risk of a transition into Recession is not zero (CP 14 at 3%, CP 15 at 3%, and CP 16 at 1%).

Retail cycle conditions in the third quarter of 2017, at CP 9 for Memphis, and published transition probabilities give calculated probabilities for future quarters. [Evans, R.D., and Mueller, G.R., “Five Property Types’ Real Estate Cycles as Markov Chains,” International Real Estate Review (2016) 19:3, 180-188.; Evans, R.D., and Mueller, G.R., “Retail Real Estate Cycles as Markov Chains,” Journal of Real Estate Portfolio Management (2013) 19:3, 95-119.] As is generally the case for real estate cycle forecasts from a Markov chain model, one step ahead probabilities are highest for no change—CP 9 has a 0.67 probability. Other Expansion cycle points, including CP 8 (at 0.03), CP 10 (at 0.16) and CP 11 (at 0.07), dominate our anticipations. Over the next four quarters, expected relative frequencies for operations are 4% for CP 7, 4% for CP 8, 43% for CP 9, 22% for CP 10, 14% for CP 11, 13% for CP 12, 3% for CP 13, and 1% for CP 14.

We know that the Memphis apartment market was in Hypersupply’s CP 12 in the third quarter of 2017. A team of Glenn Mueller and Andrew Mueller (both at University of Denver), and Memphians Shawn Massey and Richard Evans presented apartment market transition probability estimates at a convention presentation. [“Forecasting Occupancy Rates in Apartment Markets with a Markov Chain Model,” April 2017 American Real Estate Society convention, now under academic peer review at the Journal of Housing Research.]  The model gives a 0.66 probability that there will be no change in the next quarter, and probabilities of “backward” transitions to CP 11 (0.10 probability), and CP 10 (0.04)‒all still very strong cycle conditions. Moving to CP 13 and its more extreme Hypersupply conditions has a 0.17 probability. Other cycle points are possible, but the Hypersupply‒Recession transition CP 14 is the only one with a probability that rounds to two percent. Across the four quarters ahead, CP 12 will prevail in an estimated 46% of the span of time, while CP 13 has a 19% expected relative frequency and CP 14 a 7% expectation. Profitable conditions in CP 11 (expected relative frequency at 14%) and CP 10 (6%) are in the list of possible strong operating environments for Memphis apartment markets, but Recession’s CP 15 has a 3% expectation.

Finally, Memphis hotel markets were in early Expansion’s CP 7 for the third quarter of 2017. Updated transition probabilities are ready for a presentation planned for April 2018 by a team: John Corgel (Cornell University), Richard Evans (University of Memphis), Bram Gallagher (CBRE Hotels’ America Research), Andrew Mueller (University of Denver), and Glenn Mueller (University of Denver). [“Using Probabilities Across Hotel Market Cycle Points to Anticipate Revenue Risks,” American Real Estate Society Convention] Memphis has a calculated 0.62 probability of staying in CP 7 for the fourth quarter, but a 0.22 probability of reaching CP 8, where new construction is financially feasible across the city market. There is risk of slipping back to less attractive cycle conditions, a 0.08 probability of CP 8, also a 0.03 probability of advancing to CP 9. For the next four quarters, the expected relative frequency is 39% for operating in CP 7, 25% for CP 8, and 10% for CP 9, but 11% for CP 6.

The Role of Real Estate Stimulus for Economic Development in the Context of the Real Estate Cycle Model

The Mueller real estate cycle model does not have clear suggestions for how a city can stimulate real estate development by giving property tax incentives to builders or firms wanting to build. The incentives are not required at CP 8, the earliest stage where eager debt and equity investors can fund new construction. This is especially true for retail and apartment markets, where incentives may have weaker labor market benefits than believed for office and industrial sectors. That is also true for other parts of the Expansion phase. At the market’s high point, CP 11, supply and demand are growing in tandem. Tax stimulus will bring increased risk of Hypersupply, hurting all market participants—especially the very builders stimulated to do the new construction just as market demand has begun to lag. Once recession is demonstrating oversupply, firms moving to the city market would be wise to accept the promise of tax abatement, then “bank it”, not acting until CP 8 conditions return and the tax benefit is “icing on the cake”. Property tax abatements would need “use it, or lose it” provisions when current city markets are in Hypersupply, Recession, or Recovery phases, as well as in CP 6 and CP 7.

Supply-side incentives are not the solution when oversupply and inadequate demand are facts in several of the cycle points, at least from Recession’s CP 15 to Expansion’s CP 7. When those conditions prevail, demand-side incentives can have short-term benefits to improve real estate cycle conditions, as well as draw outside firms into the city market. The demand-side incentive may be as simple as identifying vacant existing space that meets the needs of the incentives’ target tenant. If the owner of the space gets property tax incentives to agree to favorable lease terms, then three benefits come. The firm considering a move into the city market gets operating cost savings that add to other locational advantages that the city market offers. Second, the owner of existing vacant property gets a long-term tenant at a rent level that is attractive, given property tax abatement. Third, the real estate cycle conditions see an improvement citywide.