Though the real estate field has matured, an occasional look back to confirm and validate factors that impact different real estate property types is important. We are re-visiting one popular multifamily indicator called Jobs-to-Permits Ratio, also known as Demand-Supply Ratio. The analysis below explains how the simple calculation helps explain the apartment market fundamentals. Though the indicator is simple to understand and plenty of evidence exists to support the strength of this indicator to explain apartment market fundamentals, disagreements do exist among economists/analysts. Those who disagree don’t seem to completely understand this indicator and jump to wrong conclusions. These “analysts” are used to complex but gibberish variables inputted in an equation to explain apartment market phenomena, where statistics, sound real estate economics, and practitioners’ view of the industry are all out of whack.
The chart shows one of the popular multifamily market indicator: Jobs-to-Permits Ratio aka Demand-Supply Ratio. The indicator takes two core drivers of multifamily, jobs and permits, and combines them into one. The math is simple: take the current period job gain and divide that by the one-year lag in multifamily permits. The Jobs-to-Permits Ratio is also called the Demand-Supply Ratio because jobs are thought of as a core demand driver, and today’s permits, in most cases, become new supply within a year. The long-term multifamily equilibrium ratio stands at 5, but since the Great Recession it has increased to 7. The higher ratio, especially greater than 5, reflect strong multifamily market fundamentals.
The simplicity of the calculation, where one number explains both demand and supply fundamental, is hard to come by and useful from analysts to executives. Historically speaking, the movement between the ratio and apartment rent, and revenue growth show high correlation. The relationship is causal as well. However, if the apartment market fundamentals go away from the ratio, it either means deeper nuances exist in the metro area, or the fundamental data you are using is flawed and it is time to switch your data provider. Those of you using NCREIF data for total return will also find very high correlation between the ratio and apartment total return.
We can clearly tell that the indicator is a result of an extension of factors that impact the apartment market. Here comes academia: When analyzing apartment markets, Econ 101 tells us that shift in demand is caused by determinant of demand. They are:
· Income: Captured by Jobs and Median Household Income.
· Number of Buyers: Captured by Population, Household Formation, Net Migration.
· Expectation: Consumer Confidence on Economy, Rental and For Sale Market.
· Prices of Related Goods and Services: Substitution Effect from Condo and Single Family Markets.
· Taste and Preferences: This is a micro variable thus hard to capture with data. But homeownership rate, especially by age group, tell us their preferences.
However, our own vigorous test of many variables suggested by the economic theory above concluded that there are 3 core variables that impact apartment market fundamentals: Job Gain/Growth, Single Family housing variables (preferably Housing Affordability Index or HAI since this variable includes combination of variables), and New Supply (Multifamily or Total Residential Permits). These 3 variables not only pass all statistical tests, but they are well accepted by practitioners of real estate in the multifamily field. Why not use various income and net migration variables as a driver as the academics suggest? Because income and migration data are already captured by job gain variable. In a metro area, a larger number of net migration flow is due to job growth, and income growth is only possible if there is a tight labor market. Also, we try to use those variables with the least amount of data collection errors. Thus, in an investment environment, if you could not explain market phenomena using these three core variables to support your deal, maybe it is a thumbs down.
The ratio cannot be mistaken for absorption, another derived variable used in the industry from occupied apartment units (apartment existing units multiplied by occupancy). Absorption is change in occupied units from the current to previous period. Because the occupied units uses existing units on its calculation, it is heavily influenced by new supply. Unlike Jobs-to-Permit Ratio or Demand-Supply Ratio the calculated absorption number is completely different indicator and absorption is hard to explain to layman.
Summary Explanation of the Chart
· Area 1. Immediately after the Great Recession, apartment new supply remained well below the historical average but labor market started to recover with job gain averaging over 2.1 million jobs per year. The ratio was well above the LTA during 2011 through 2013. Pent up demand fueled by job growth increased apartment market fundamentals.
· Area 2. After 2013, jobs increased but magnitude of increase in multifamily permits was higher, decelerating the ratio. Though job gain was slower than in prior recoveries, there was a consistent job production keeping the multifamily fundamentals above average during those years.
· Area 3. The ratio hovered slightly below 5 during 2019, as job gain started to simmer down while new supply kept increasing. With historic low unemployment rate, the labor market remained tight though nominal job gain decreased compared to previous years in 2019, keeping the apartment market healthy.
· Outlook: Expect deep drop in the ratio in 2020, which translates to drop in apartment market fundamentals.
Ø Since the pandemic, there has been spurt of some good news surrounding the apartment market industry but investors should keep the focus on the core driver of apartment market, which is job growth (Watch out for salience bias). We will post our new forecast after the 2Q20 job growth actual data become available.
Ø For those using data subscriptions from varied sources, if there is no transparency on the data collection and analytics, they are selling you “snake oil”. And if they do let you get to the nitty-gritty of their data and methods, look for gibberish equations and explanations. If you will catch it, you will get a good laugh!!