(continuing from the front page …) On the other hand, landlords have more pricing power. However, new landlords (aka. property investors) in 2022 are confronted by the fact that property prices around them have significantly increased and at current rents (rents that existed before their purchase of the property) that their existing rates of return have fallen. Thus, there is an incentive for landlords to raise rents in the period after rising home prices. This introduces five questions: (1) does rent appreciation mimic home price appreciation in the months following home price appreciation, if so, (2) what is the maximum magnitude; (3) what is the lag, (4) is the relationship identical across cities (and) 5) does a fall in home price appreciation lead to rent appreciation slowing 12 months later?
Here, we limit our discussion to median rents charged each month for three-bedroom single family residential properties. Thus, rent data by construction consists of rents that were newly contracted that month combined with all the rents which were contracted the prior 11 months. Let rental rate appreciation be defined as RRA = %ΔR where R is the median rent in given city. Also let home price appreciation be defined as HPA = %ΔP where P is the median price of a three-bedroom home. Thus, my rent data (R) is a rolling average of prior months whereas the sales price data (P) reflects sales that transpired that prior month.
We can use a regression to understand the historical relationship between RRA and HPA, and the vacancy rates, twelve months prior. Let,
Let RRA = α + β HPA(t-12) + δ vacc(t-12) + e
We can do this for each of 20 selected cities. The 20 values of β are the regression estimated average relationship between RRA and HPA(t-12) for each city. We see 20 values of β in Chart 1. Chart 1 is a static snapshot of all prior period’s information and how rental markets have responded to activity in the purchase market twelve months earlier. The βs reflect the response by landlords to pricing rents in period t, to changes in the cost of purchasing an investment property 12 months earlier.
What we notice in Chart 1, is that for most cities (but not all) there is a positive relationship between HPA and RRA measures by the βs. We also notice in Chart 1 that the impact of HPA varies depending on the city. The positive values on the vertical axis ranges from .02 to .30 The βs measure the response by RRA to a 100-bps increase in HPA twelve months earlier. For example, the cities of Los Angeles, CA, San Francisco, CA and New York, NY all have a β value around 0.15. Thus, if HPA was running 100 bps faster in each of those three cities twelve months ago, we would expect RRA to be 15 bps faster now.
Home price appreciation in Los Angeles, CA, San Francisco, CA and New York, NY in Jul-21 were 17%, 18% and 15%, respectively, higher than they were in Jul-20. We might have expected landlords to raise rents on single family properties in Jul-22, in those cities, by about 2.5% (= 17% * 0.15). In fact, they rose by 8.0%, 1.5% and 8%, respectively. So, we see a large model error. Nonetheless, we see information that higher HPA impacts RRA in 12 months in most cities.
Cities in America vary in terms of their economic robustness. One way to measure the economic health of a city is by the city’s average credit score. The βs measure how strongly the purchase market twelve months ago influences the rental market. The βs can be considered as a speed of adjustment at the 12-month mark. Chart 2 shows that rental markets are less responsive to the purchase market in cities with better credit scores.
Mortgage rates now (during Sep-22) were roughly 300 bps higher than a year ago (Sep-21). What does that likely mean for HPA by Sep-23. Higher mortgage rates have slowed down HPA. We have shown in Charts 1 and 2 that by regressing rent appreciation against home price appreciation, for the historical period of Jan-13 through Jul-22, holding other things constant, we can get a slower rent appreciation for each city twelve months after the HPA change. So, we should expect rent appreciation to slow (not go negative) by Sep-23.
To obtain the βs in Chart 1, we restrict both the rent data and home prices data to observations based on three-bedroom detached properties. The data comes from Altisource.com and extends from Jan-13 and goes to Jul-22. In a separate report on CHTR’s front page, we show a second measure of βs, where we utilize an average rental rate appreciation based on apartments of all sizes in both multi-unit buildings and multi-unit single family detached properties and include the monthly rent on single family detached properties. The sales prices data remains as the price on three-bedroom properties. The data for that second report is from Zillow.com and extends over the same period. A city is measured as a Core Based Statistical Area, or CBSA in this report.