Thursday, 7 April 2022

What landlords see as important when they set rents

 There is a famous quote, attributed to economics Nobel laureate Ronald Coase, that reads “If you torture the data long enough, it will confess to anything”. Unfortunately, based on my experience this week, that doesn’t appear to be the case. I’ve spent two full days playing with data from a survey a student of mine collected from landlords (members of the NZ Property Investors Federation) back in 2018. The goal was to derive some insights into the factors that landlords see as important when they set rents, and whether those that place a greater importance on tenant attributes are more likely to set rents that are below-market. Unfortunately, I’ve concluded that the data tell us nothing of substance. So, with that in mind, and no prospect of generating a compelling research article from the data, I’ve decided to dump the few interesting bits into this blog post instead.

The genesis of this research was this 2016 post I raised the possibility that landlords offer ‘efficiency rents’:

There are good tenants and bad tenants, and it is difficult for landlords to regulate tenants' behaviour after they have signed the rental agreement. Given this is moral hazard and efficiency wages is one way to deal with moral hazard in labour markets, is there a rental market equivalent of efficiency wages?

First, some context. In ECON100 and ECON110, we discuss moral hazard and agency problems. One such problem is where employees' incentives (after they have signed their employment agreement) are not aligned with those of the employer. The employer wants their employees to work hard, but working hard is costly for the employee so they prefer to shirk. One potential solution to this is efficiency wages (I've previously discussed efficiency wages here). With efficiency wages, employers offer wages that are higher than the equilibrium wage, knowing that this will encourage higher productivity and lower absenteeism from their workers. This is because if workers don't work hard (and avoid absenteeism), they may lose their jobs and have to find a job somewhere else at a much lower rate.

Which brings me to landlords and efficiency rents. As noted above, there is a moral hazard problem for landlords - tenants' incentives (to look after the property) are not aligned with the landlord's incentive (to keep the property in top condition). If the landlord instead offered an efficiency rent (a rent below the equilibrium market rent), then they would have many potential tenants applying for the property, allowing the landlord to pick the best (the least likely to damage the property). It also gives the tenants an incentive to look after the property after signing the tenancy agreement, because if they don't they get evicted and have to find another place to live at a much higher cost.

Maybe landlords offer efficiency rents already and we just don't realise it? 

That’s what we set out to test in 2018. We engaged the NZPIF, and they agreed to support the survey by sending it to their members. We don’t know how big the membership base it (possibly in the thousands), but we had 104 responses to the survey, and 93 of them gave us enough data to be useable for analysis. The median landlord had five properties, and the range was one property to 120 properties.

Do landlords offer below-market rents? Some clearly do (or at least they say that they do). We asked separately about existing tenancies and new tenancies, and 37 out of 93 told us they offer below-market rent to existing tenancies, while 14 out of 93 told us they offer below-market rent to new tenancies. So far, kind of interesting. There were 24 landlords who said that they offered below-market rent to existing tenancies, while also saying that they offered market rent of above-market rent to new tenancies. I took those as indicative of efficiency rents, reasoning that landlords have less imperfect information about existing tenants than new tenants, and so landlords would opt to offer lower rents as they don’t want to lose ‘good’ tenants (this approach has a theoretical basis too – see here).

Unfortunately, it turns out that my measure of efficiency rents is completely unrelated statistically to anything else we know from the survey. Large and small landlords, whether they use property managers, whether they engage in regular rent reviews, the location of the property, etc. are not correlated with my measure. Essentially all I can conclude from that is that whether a landlord offers below-market rent or not is based on unobserved characteristics of the tenant or the property. I guess that is the point of efficiency rents – we don’t observe the quality of the tenant, but the landlord will have discovered some information about tenant quality that we don’t observe. Still, that is pretty unsatisfying as it leaves the survey approach somewhat worthless.

We also asked landlords about what factors (of a total of 21 factors) were important in their rent-setting decisions. We asked these questions in two ways. First, we asked about setting rents ‘on average’ for their properties. We later asked them about a single property, selected a random (the randomisation mechanism here was quite cute – we asked them about the property that is located on a street starting with the letter that is closest to the first letter of their surname [*]), at the last time the property’s rent was set or reviewed. There aren’t systematic differences in the rankings between the two ways we asked (which may again point to idiosyncratic differences in rent setting related to unobserved characteristics of tenants or properties), so I’ll focus on the ‘on average’ results.

We asked the landlords to rate each factor on a five-point scale. Some rated all or most factors important, and others rated all or most factors unimportant, so I standardised the ratings within each landlord, to a measure with a mean of zero and a standard deviation equal to one. Summarising the results for the 93 landlords overall, we get this:

The bars represent how important (on average) each of the factors is. A positive number represents more important on average, and a negative number represents less important on average. The colours of the bars group the factors into different categories (profitability factors; cost factors; local demand factors; property factors; and tenant factors). Overall, on average it appears that the most important factors are the level of rents in surrounding areas, and local demand for rental property (no surprises there). After that, property factors (number of bedrooms, and location and amenities) are important. Least important is local demand from property buyers. That makes sense too. Potential capital gains don’t appear to matter, and property management costs are less important as well (probably because only 43 of 93 landlords used a property manager).

However, the importance of these factors doesn’t appear to differ much based on landlord characteristics (at least, not in a way that makes sense). And the importance ratings are not related to my measure of efficiency rents.

All up, this research didn’t tell us much (at least, not to an extent that makes it publishable other than in a blog post!). That is somewhat disappointing, because there isn’t a large literature on this, and most that exists is theoretical rather than empirical. A better approach for further research might be to look at matched tenant-landlord data, but it’s not clear that such data exists (tenancy bond data is available for New Zealand, but I’m unsure how much data on landlords is captured, or how much data on tenants). I’ll leave that for future work, if I have the energy and inclination (or a motivated student) to work on it again.

*****

[*] This isn’t a perfect means of randomisation, of course. However, I reasoned that approach was better than asking about the property that they last conducted a rent review for (which seems an obvious choice for randomisation). That would be problematic, since the frequency of rent reviews may differ between good and bad tenants, and therefore we would be more likely to receive data on a low-quality tenant or low-quality property. Our approach avoided that problem.

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