Kansas City rent strike rebalances relative bargaining power towards tenants

Today in my ECONS101 class, I covered search models of the labour market. In these models, a matching between a worker and an employer creates a surplus, which is then shared between the worker and employer depending on their relative bargaining power. The greater the worker's relative bargaining power, the greater the share of the surplus the worker will claim, meaning that wages will be higher. The lesser the worker's relative bargaining power, the lesser the share of the surplus the worker will receive, meaning that wages will be lower.

Search models are not just useful for thinking about labour markets though. They can be used in any situation where two (or more) parties are matched together in a way that creates a surplus, which is then shared between them. A joint venture between firms is an example. So is a marriage. In both cases the parties match together, create a surplus, and then share that surplus based on their relative bargaining power. A further example exists in the market for rental housing. Landlords and tenants are matched together. That matching creates a surplus, which is shared between them. And relative bargaining power matters, as this Yahoo!News article from the end of last year (originally published in the Washington Post) demonstrates:

In the two years she has lived at Bowen Tower, Cynthia Barlow’s apartment has flooded, been plagued by mold and been infested with cockroaches. The building’s heat stopped working. When the elevators broke over the summer, emergency workers carried a sick neighbor down 10 flights of stairs.

Meanwhile, Barlow’s rent for the two-bedroom unit increased from $993 per month to $1,213.

Growing frustrated, she hung fliers in the elevators and hosted potlucks, persuading a majority of tenants in the 90-unit building to join the Bowen Tower Tenant Union and stop paying rent until conditions improved. So far, they’ve won a meeting with the landlord, and a judge has knocked thousands of dollars off the rent debt of one resident facing eviction.

“I got tired of being treated the way I was treated,” Barlow said.

The rent strike is part of a strategy that housing activists have started to replicate in midsize cities across the country.

When tenants organise themselves into a tenant union, then ceteris paribus (holding all else equal) that increases the tenants' relative bargaining power with the landlords. If the tenants were to all leave their apartments, then the landlord has to search for new tenants, which is costly. Now, an individual tenant could threaten to move out, but filling one apartment with a new tenant is relatively easy. When an entire block of tenants makes the same threat, the landlord is facing serious disruption. More importantly, a rent strike means that, instead of losing or negotiating with one tenant at a time, the landlord faces coordinated action including withholding rent, legal disputes, repair demands, and public pressure from many tenants at once.

The tenants' increased bargaining power (due to unionising) should result in lower rents and improved maintenance of the apartments. The way this worked in practice was that tenants, feeling more powerful with the backing of other tenants, stopped paying rent. However:

Barlow is scheduled for eviction court in January, and Bowen Tower management hasn’t renewed her lease.

There is only so far that tenants can push their greater relative bargaining power, particularly where alternative affordable housing is scarce and legal protections are weak. However, the Bowen Tower case also shows why collective action may matter. Ultimately, the tenants appear to have prevailed and won substantial concessions. This later article notes that the rent strike ended after four months, when the landlord promised repairs and lower rents, and after tenants had withheld nearly US$110,000 in rent. 

Tenant unionisation does not guarantee success, and the risks to tenants can be substantial. But it does mean that tenant unions can shift the bargaining outcome, and the share of the surplus, especially when they are able to sustain coordination long enough to make the landlord’s alternative more costly than negotiation.

[HT: New Zealand Herald]

Two papers show the bad, and some good, of rent control in San Francisco

I have been talking with my ECONS101 class this week about rent controls, which is a topic that I have blogged about many times before (see the links at the end of this post). Economists really dislike rent controls, sometimes in deliberately hyperbolic terms. In one prominent case, the Swedish economist Assar Lindbeck was quoted as saying:

“Rent control appears to be the most efficient technique presently known to destroy a city—except for bombing.”

Lindbeck's statement is based on the evidence that shows the negative impacts of rent controls. One example is described in this 2025 article by Eilidh Geddes (University of Georgia) and Nicole Holz (Northwestern University), published in the Journal of Housing Economics (ungated earlier version here). They looked at the impact of a large-scale rent control expansion in San Francisco in 1994, which removed an exemption from rent control for small (less than five units) owner-occupied buildings built before 1980, on evictions.

Their data are the number of eviction notices, as well as wrongful eviction claims and 'owner move-in' eviction notices at the zip code level, from 1990 to 2010. They apply a continuous treatment difference-in-differences, which essentially compares the change in evictions (or other measure) between zip codes that were more affected by the removal of the exemption and those that were less affected. Their measure of exposure to the treatment is the number of housing units in the zip code that became exposed to rent control policies after the passage of the voter referendum in late 1994. In zip codes where more housing units were affected by the change, we would expect to see greater impacts than in zip codes where fewer housing units were affected. One limitation of this is the data source that Geddes and Holz use, which is based on building data from 1999, five years after the change was implemented. However, they show that three main sources of problems (demolition of buildings between 1994 and 1999, splitting of land parcels, and construction that changed the number of units in each building), do not have much impact on the estimated number of units affected (and so, don't have a large impact on the treatment variable).

In their main analysis, Geddes and Holz find:

...an 83% increase in eviction notices filed with the Rent Board and a 125% increase in the number of wrongful eviction claims for ZIP codes with the average level of new exposure to rent control...

These effects are large and economically significant. We find an annual effect of an increase of 20.07 eviction notices per 1000 treated units in a zip code. Over the six years in our post period (1995–2000), this translates roughly into 12% of newly rent controlled units receiving an eviction notice.

So, the expansion of rent control leads to an increase in evictions. Geddes and Holz also find that the effects:

...are concentrated in low-income areas. These areas are not necessarily those that saw the largest increases in aggregate rents during the 1990s, suggesting that landlords may be more willing to engage in eviction activity in places where there are fewer resources to fight that behavior.

Geddes and Holz caution against taking a broad interpretation of their results though, as the removal of the exemption in 1994 primarily affected small landlords, who are often 'mom and pop' landlords and are able to take advantage of 'owner move-in' eviction provisions that are not available to large corporate landlords. However, the results are consistent with the broader literature, which suggests that tenants may be negatively affected by rent controls.

But not in all ways, it appears. In a more recent article published in the Journal of Health Economics (open access), Geddes and Holz look at the impact of the same 1994 expansion of rent control in San Francisco on intimate partner violence (IPV). They first note that that the effect of rent control on IPV is theoretically ambiguous, and there are two competing models with different predictions:

In the financial strain model, lower housing costs will decrease financial stress, leading to lower levels of violence. The effect of housing policies will thus depend on whether they lower costs for couples. However, in a bargaining model, there is a crucial distinction between policies that shift housing costs overall and those that shift the relative costs of housing inside and outside of the relationship. Policies that decrease housing costs overall will change the amount of resources in the relationship to be bargained over, but will not shift the bargaining power in the relationship. However, policies that decrease housing costs inside the relationship relative to those outside of the relationship will change the attractiveness of the outside option, shifting bargaining power away from the woman.

The empirical setup in this research is the same as for their earlier research on evictions. The difference is that the outcome variable of interest is IPV, measured as:

...the number of hospitalisations resulting from assaults that comes from California’s Department of Health Care Access and Information (HCAI, formerly OSHPD) from 1990–2000.

In their main analysis, Geddes and Holz find that:

...for every one percent increase in exposure to rent control in a ZIP code, hospitalized assaults on women decline by 0.08 percent. In levels, this translates to an almost 10 percent decrease in violence against women for the average ZIP code.

They find no corresponding decrease in assaults on men, which suggests that their results are not driven by an overall decline in assaults (including non-IPV assaults). They also find no effect on reported accidents, which suggests that their results are not driven by changes in the propensity to report IPV. Interestingly, they also find:

...no evidence of changes in household size or composition, suggesting that our results are driven by changes in violence within relationships rather than changes in cohabitation or relationship dissolution.

Overall, their results are most consistent with the financial strain model of IPV. Based on that model, we interpret these results as showing that rent controls, by reducing housing costs (and it is worth noting that housing costs in San Francisco are, and have been for some time, very high), decrease conflict within intimate relationships, and decrease IPV.

So, at least there is some evidence for positive effects of rent control. These results also sit alongside earlier evidence from the same rent control expansion, which showed short-run gains for incumbent tenants, but long-run reductions in the supply of rental housing units, as well as an increase in inequality. However, few people are advocating for rent control policies in order to reduce intimate partner violence. And benefits in terms of reduced violence have to be weighed against all of the other negative consequences of rent control policies, many of which are outlined in the posts linked below.

Read more:

• Why rent controls have worse effects over longer time horizons
• The deadweight loss of rent control
• The deadly consequences of rent control in Mumbai
• Rent control and inequality in San Francisco
• Rent control and the redistribution of wealth
• Rent control and vacant properties in India
• Rent control according to Seinfeld
• There should be no debate at all about rent controls
• Rent controls make many tenants worse off in the Netherlands

Why the accommodation supplement does little to help low-income tenants

In an article in The Conversation earlier this year, Edward Yiu and William Cheung (both University of Auckland) discuss New Zealand's accommodation supplement for low-income renters:

New Zealand’s unaffordable housing market has left many low and middle-income families reliant on the accommodation supplement to cover rent and mortgage payments.

But our new research has found the scheme, which costs the government almost NZ$2 billion a year, might not be an effective tool in addressing the country’s housing affordability crisis.

Introduced in 1993, the accommodation supplement is a weekly, means-tested payment designed to subsidise part of a household’s rent or mortgage. The supplement is calculated using the actual rent or mortgage payments a client is paying.

But our study looking at data from Auckland between 2019 and 2023 found accommodation supplement rental subsidies were not delivering meaningful improvements in affordability for renters.

That the accommodation supplement doesn't deliver improvements in affordability for renters is consistent with a simple model of the market for rental housing, as shown in the diagram below. This is the stylised version of this market that I use in my ECONS102 class, and is based on the rental market for land. In this market, the supply curve is very inelastic (very steep), and starts at a positive quantity (meaning that if rent falls to zero, there is still a positive quantity of land that is made available to rent). That's because of two reasons. First, this market includes owner-occupiers. They would rent land to themselves, even if the rent falls to zero (that explains the positive quantity when the rent is zero). Second, the quantity of land supplied doesn't respond very much to the rent - landlords can't suddenly make more land available - as Mark Twain once noted: "The thing about land is, they aren't making it anymore". Twain isn't quite correct, as land can be reclaimed from the ocean. However, landlords are unlikely to be very responsive to changes in rent, making the supply curve very inelastic.

Now, consider this market operating at equilibrium (with no accommodation supplement). The market operates at the point where supply meets demand, at a rent of R0, with Q0 housing (technically, land) rented. The accommodation supplement acts as a subsidy, paid to the tenants. We show this on the diagram with a new curve, D+subsidy, which lies above the demand curve D. It acts like an increase in the demand for rental accommodation. The price that landlords receive for housing increases to RL. That is the rent that tenants pay to the landlords. However, once the accommodation supplement is subtracted, the effective rent paid by the tenants decreases to RT (the difference between RL and RT is the amount of the accommodation supplement).

But notice the difference in the rents with the accommodation supplement to the equilibrium rent. The rent that landlords receive increases by a lot (from R0 to RL). The effective rent paid by tenants is barely affected (decreasing from R0 to RT). Landlords benefit the most from the accommodation supplement, with tenants barely benefiting at all. That is because the side of the market (supply or demand) that is more inelastic will always capture most of the gains from a subsidy. In this case, the supply is very inelastic (and certainly more inelastic than demand), so landlords stand to gain most from the subsidy.

It gets worse though. The increase in rents that landlords receive also affects rents paid by tenants who don't receive the accommodation supplement at all. These higher-income tenants pay higher rents as well, because they have to compete with the subsidised tenants for housing. However, the government doesn't provide them with any subsidy, making them clearly worse off as a result.

So, it should be no surprise that the accommodation supplement does not deliver meaningful improvements in affordability for renters. It barely has any effect on the effective rent paid by tenants who receive the accommodation supplement, and raises the rents paid by tenants who don't receive the accommodation supplement.

Are there better options? Yiu and Cheung suggest that:

...mortgage support seems to level the playing field more effectively than rental assistance.

Possibly. If a mortgage subsidy allows some low-income tenants to become owner-occupiers instead, then they will benefit greatly (from capital gains, as well as many other benefits associated with home ownership). If the mortgage subsidy is given to landlords as well, it might help to lower rents. Overall, it could well be more effective than the current accommodation supplement paid to tenants. It is certainly something worth further exploration.

Local minimum wages and low-quality housing rents in Japan

In this 2023 post, I discussed the impact of higher minimum wages on homelessness. Part of the story related to housing rents:

There are a couple of reasons to expect that higher minimum wages might increase homelessness. If minimum wages decrease employment (a result that is contested, but I believe it is likely given the galaxy of literature we have to date; again, see the links at the end of this post), then higher minimum wages may directly increase the risk of people becoming homeless. That's because when low-income people workers lose their jobs, they may no longer be able to afford to pay rent, and may lose their homes. Second, if minimum wages increase incomes for those that are not made unemployed, they may increase the demand for housing, pushing up rents. This may indirectly increase the risk of people becoming homeless, who can no longer afford the higher market rent.

The research that I referred to in that post found that higher minimum wages increased homelessness, and that they also increased housing rents, consistent with the mechanism outlined above. However, we shouldn't believe just a single paper's research findings. This 2021 article by Atsushi Yamagishi (Princeton University), published in the journal Regional Science and Urban Economics (ungated earlier version here), provides some additional evidence, this time from Japan.

Japan provides an interesting case study for examining the effects of the minimum wage on housing, because:

Japan has forty-seven prefectures and each has a different minimum wage rate. There is no difference in the minimum wage rate within a prefecture...

And on top of that, each prefecture has little control over its local minimum wage. Yamagishi notes that:

...the minimum wage setting in Japan is highly centralized and unresponsive to trends in local housing markets due to institutional features. Japanese prefectural minimum wages are determined by the following process. First, the central government classifies prefectures into four categories, and it assigns the targeted amount of minimum wage increase to each category. The categorization is reviewed only once every five years and changes in the classification are rare.

Yamagishi uses data from 2007 to 2013, and exploits an interesting natural experiment, where:

From 2007 to 2012, a new consideration took the primary role in setting minimum wages due to the national policy change... after the revision of the Minimum Wage Law in 2007, the primary consideration in setting the minimum wage rate became closing the gap between the quality of life of minimum wage workers and people relying on Public Assistance (seikatsu-hogo, PA henceforth)...

Since the gap was generally larger in urban areas, the policy resulted in a plausibly exogenous minimum wage increase in urban prefectures...

So, not only is there variation in minimum wages across prefectures, and that variation is not related to local housing markets, there is a change in the variation driven by the policy change. Yamagishi uses data on advertised apartment [*] rents from At Home, "one of the most popular online real estate search engines in Japan". Using both an event study research design and a difference-in-differences design, Yamagishi found that:

...low-quality apartments experience around a 2.5-4.5% rent increase in response to a 10% minimum wage increase.

When looking at differences by apartment quality (proxied by the age of the apartment, with 'old' apartments being over 25 years old and 'very old' apartments being over 35 years old), Yamagishi found that:

An old apartment experiences a rent increase of around 3.3% when the minimum wage increases by 10%, which is statistically significant at the 1% level. A very old apartment experiences an increase of around 4%, which is also significant at 1% level. Overall, the result reveals the larger impact on the rents of lower-quality apartments...

Of course, the key point here is that workers on the minimum wage are more likely to live in low-quality apartments than in higher-quality apartments. So, the takeaway from this paper is that higher minimum wages may make some workers better off in terms of higher wages (while also considering the disemployment effects of the higher minimum wage), but that the gains of those workers would be offset somewhat by higher rents. Yamagishi estimates that landlords gain between 7.5-13.5 percent of the increased minimum wage, but the assumptions necessary to arrive at that estimate are a little difficult to justify.

Nevertheless, the overall point stands. The minimum wage workers lose some of their higher minimum wage to higher rents.

[HT: Marginal Revolution, back in 2023]

*****

[*] As an interesting aside, Yamagishi notes that in Japan, 'apartments' are generally low quality. A high quality apartment is referred to as a 'mansion' (see here).

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data
• The minimum wage and homelessness
• New York restaurants find a new way to respond to the minimum wage
• The minimum wage and workplace safety
• How employers respond to minimum wage increases
• Minimum wage increases and employer noncompliance
• Higher minimum wages and poverty revisited
• Minimum wages and health
• Effects of the minimum wage on the nonprofit sector
• 15 years of US research on the minimum wage elasticity of employment

Rent controls make many tenants worse off in the Netherlands

Rent controls have created shortages of housing, every time and in every place that they have been tried. In the latest futile attempt to create working rent controls, the Netherlands has worsened its housing shortage. As Bloomberg reported recently (paywalled, but try this alternative link):

Two years ago, Nine Moraal and her two children moved into a one-bedroom flat near the Dutch city of Utrecht, a comfortable spot close to family and friends. Although she had only a two-year lease, she expected to be able to extend it and stay until she could get one of the Netherlands’ many rent-controlled apartments.

But last spring, her landlord told her she’d have to move out in November, because renting the flat was no longer profitable. Despite “frantic efforts on social media, phone calls, visits to realtors and housing agencies,” the 33-year-old educator says she hasn’t found anything. “The cost isn’t the problem, but a real shortage of housing is.”

Moraal is among the growing number of Dutch people struggling to find a rental property after a new law designed to make homes more affordable ended up aggravating a housing shortage. Aiming to protect low-income tenants, the government in July imposed rent controls on thousands of homes, introducing a system of rating properties based on factors such as condition, size and energy efficiency. The Affordable Rent Act introduced rent controls on 300,000 units, moving them out of the unregulated market...

For the past year, Shahmy Wahabdeen has been renting a house in The Hague for €1,400 a month. After the new rules kicked in, his landlord decided to sell, leaving Wahabdeen scrambling to find new digs for his family of four. “I’m feeling completely hopeless and am seriously considering sending my family back home,” says the 34-year-old software engineer from Sri Lanka. “I don’t know what else to do.”

Coincidentally, I covered rent control with my ECONS101 class in the lecture today. I could see some sceptical faces around the class when I described the negative impacts of rent control on the market, and especially the negative impacts on tenants. However, there is lots of robust evidence on these negative effects, and the Netherlands example is just one more example of how rent controls often fail to help the very people that they are designed to help. A tenant who has nowhere to live isn't going to thank the government for cheap rent.

In fact, my most recent post on this topic was titled "There should be no debate at all about rent controls", and that's because any debate should be over before it begins. When it comes to bad policy, rent control ranks near the top. It certainly isn't a way of fixing the cost of housing. If a government is concerned about the cost of housing, they should build more housing.

[HT: Marginal Revolution]

Read more:

• Why rent controls have worse effects over longer time horizons
• The deadweight loss of rent control
• The deadly consequences of rent control in Mumbai
• Rent control and inequality in San Francisco
• Rent control and the redistribution of wealth
• Rent control and vacant properties in India
• Rent control according to Seinfeld
• There should be no debate at all about rent controls

The post-COVID-19 housing boom as a cause of the US Great Resignation

The term 'Great Resignation' came into use last year to refer to a sustained decline in the labour force participation rate in the US. As I noted last year, there is little evidence that there has been a Great Resignation in New Zealand, despite media commentary suggesting that there has been. However, this working paper from last year, by Jack Favilukis and Gen Li (both University of British Columbia), is making me wonder why New Zealand didn't experience a Great Resignation.

Favilukis and Li look at whether the COVID-19 housing boom explains the US Great Resignation, using data from the American Community Survey. They start by showing that the Great Resignation was concentrated among older workers. Specifically:

First, up to 2019, the labor force participation rate was rising for all age groups, but especially for older Americans. Second, in 2020, labor force participation fell for all groups, but most dramatically for the youngest and oldest Americans. Third, with the exception of the oldest Americans, all groups returned to the labor force in 2021 and were near or even above 2019 rates by 2022. On the other hand, the oldest Americans further reduced labor force participation in 2021 and continued to stay out of the labor force in 2022. By 2022, nearly the entire reduction in the labor force participation rate was due to the 65+ group. This is especially striking given the pre-2020 trend.

Favilukis and Li then look at whether employment, or weekly hours worked, are related to the housing market return over the past 4.5 years. They estimate this relationship separately for each year, age group, and homeowners/renters, allowing them to look at whether these various groups were affected in different ways by the post-COVID-19 housing boom. The results don't appear to be particularly sensitive to the choice of 4.5 years of housing market returns as the key variable, with other lengths of time showing similar results.

They find that, for 2021:

Renters tend to increase their labor force participation in response to house price increases; a 40 year old renter increases her probability of being in the labor force by approximately 0.07×0.10 =0.7% for every 10% increase in house prices (e.g. from 80% to 80.7% participation). This is statistically significant... Older renters are less reactive to house price changes, with slopes slightly positive but rarely statistically significant.

Younger owners also increase their labor force participation in response to house price increases, although their response is much lower than that of younger renters... Middle aged owners are relatively unresponsive to house price changes – their labor force participation falls slightly in response to higher prices but this is not statistically significant for one year age buckets.

However, individuals above 60 have a strong negative response; a 65 year old owner decreases her probability of being in the labor force by approximately 0.11 × 0.10 =1.1% for every 10% increase in house prices (e.g. from 15% to 13.9% participation). This is strongly statistically significant...

So, it appears that the 'Great Resignation' was concentrated not only among older workers, but among older workers who are homeowners, rather than renters. And, the propensity to be out of work within that group is strongly related to housing returns. Moreover, in metropolitan areas where housing returns were higher, the Great Resignation among older homeowners was bigger.

Which brings me back to my comment from the start of this post. Post-COVID-19 housing returns have been relatively strong (according to the QV House Price Index), and yet we haven't experienced the same substantial decrease in the labour force participation rate as seen in the US. Here's the labour force participation rate among people aged 65 years and over in New Zealand over the period since 2016 (source here):

There's little evidence of a Great Resignation among older people in New Zealand. The trend remains generally upwards over time. Unfortunately, the freely available data don't allow us to look at the difference between homeowners and renters, but if there were similar effects to those reported in the Favilukis and Li paper, the Great Resignation among older homeowners would be apparent in the overall statistics without looking at those homeowners and renters separately.

Favilukis and Li conclude that:

High house prices allowed many older Americans to retire early; if not for the high house prices, their labor force participation in 2021 would have been similar to 2019.

However, clearly there is more to the story than simply a housing boom leading to Great Resignation, otherwise we would have likely seen the same effect in New Zealand. New Zealand house prices are high. Why didn't that induce older New Zealand homeowners to retire early? Hopefully, someone is investigating that question.

[HT: Marginal Revolution, last year]

Read more:

• The myth of the Great Resignation (in New Zealand)

Could cash grants solve the homelessness problem?

New Zealand has a serious homeless problem. We could argue about the numbers of homeless people, but it is clear that it is a serious problem when even small-town New Zealand is experiencing rising homeless numbers. And homelessness is a particular problem for Māori. However, there appears to be no easy solution. If there was, we would have done it already.

Over the last decade in Hamilton, the People's Project has adopted the Housing First approach, funded by the Ministry of Housing and Urban development. While this approach has shown some promising results, it doesn't take much walking around downtown Hamilton or Hamilton East to realise that it is far from perfect.

What if we could do better by simply offering a grant to homeless people, that they can use in any way that they wish, including obtaining housing? Would that be an effective way to reduce homelessness? That is essentially the research question addressed in this recent article (open access) by Ryan Dwyer (University of British Columbia) and co-authors, published in the Proceedings of the National Academy of Sciences (see also this non-technical summary on The Conversation). Dwyer et al. use a randomised controlled trial to evaluate the impact of giving homeless people in Vancouver a one-off grant of CDN$7.500. As they explain:

We conducted a preregistered cluster-randomized controlled trial where individuals experiencing homelessness were randomly assigned to receive a one-time unconditional cash transfer of CAD$7,500. This amount equaled the annual income assistance in British Columbia in 2016 and represented 59.6% of the average personal annual income ($12,580) of our participants. The cash transfer was provided in a lump sum to enable maximum purchasing freedom and choice (e.g., rent, durable goods), whereas smaller repeated transfers would not. To avoid benefits cliff, we established an agreement with the BC provincial government that ensured the cash transfer did not impact participants’ existing or future benefits.

Dwyer et al. limited the pool of potential cash recipients to homeless people aged 19 to 65 who had been homeless less than two years, and who did not have serious issues with mental health or substance abuse (including alcohol). The final sample included 115 homeless people, 65 in the control condition (who did not receive the cash), and 50 in the treatment condition (who did receive the cash). They randomised treatment at the homeless shelter level (so all participants from the same shelter were either in the treatment group, or the control group) to limit risks to those who received the cash. More specifically:

There were four conditions in the study: two cash and two noncash. Based on past studies showing that motivational training can help improve cognitive and behavioral outcomes for those living in poverty... we provided workshop and coaching supports in addition to the cash transfer... Workshop consisted of a 1-h session every 3 mo for 1 y, where participants were guided to complete self-affirmation, goal-setting, and plan-making exercises to help participants brainstorm strategies to regain stability in their lives...Coaching consisted of three 45-min phone calls per month for 6 mo with a certified coach trained to help participants learn from their own experiences to increase self-efficacy in developing life skills and strategies to achieve their life goals.

In condition 1, 25 participants (nshelters = 5) were provided with a one-time cash transfer of $7,500, workshop, and coaching. In condition 2, 25 participants (nshelters = 5) were provided with the cash transfer and workshop but no coaching. In condition 3, 19 participants (nshelters = 5) were provided with workshop and coaching, but no cash transfer. In condition 4, 46 participants (nshelters = 6) were not provided with the cash transfer, workshop, or coaching.

The primary outcomes that Dwyer et al. were interested in were subjective wellbeing and cognitive outcomes one month after receiving the cash grant. However, they found that there was:

...no significant interaction effect for any of the preregistered outcomes. Specifically, cash recipients did not differ from noncash participants in terms of cognitive and subjective well-being outcomes from baseline to 1 mo; cash recipients with coaching did not differ from cash recipients without coaching; and noncash participants with workshop and coaching did not differ from noncash participants without any supports.

In other words, there was no effect on what Dwyer et al. expected. However, they then explore some other outcomes, and over a longer time period of up to one year, finding that:

 Over the year, cash recipients spent 99 fewer days homeless (e.g., shelter, streets) and 55 more days in stable housing (e.g., apartment) on average than control participants... For finances, cash recipients retained more savings ($1,160) and increased monthly spending more ($429) on average than control participants... Importantly, spending on temptation goods (i.e., alcohol, drugs, cigarettes) was not different between groups.

Those are potentially important results, although we should discount them somewhat because they were not part of the pre-registered study. However, this part is potentially the most important:

By reducing time in shelters, the cash transfer was cost-effective. The societal cost of a shelter stay in Vancouver is estimated at $93 per night... so fewer nights in shelters generated a societal cost savings of $8,277. After accounting for the cost of the cash transfer, the reduced shelter use led to societal net savings of $777 per person a year. Alternatively, freed-up shelter beds can be reallocated, so the benefits can trickle down by helping others avoid sleeping on the street.

If the benefits of this intervention (in terms of cost savings from shelter stays) is greater than the costs (in terms of the cash payment, plus any administrative costs [which are not included in the calculations in the paragraph quoted above]), then that seems like a win to me. However, before we get too carried away, the authors also report on the outcomes at various time points in between one month and one year after the cash grant, where they find that:

...the overall effects were primarily driven by impacts within the first 3 mo after the cash transfer.

Looking at the number of days spent homeless, the effect is relatively large (-0.95 standard deviations) after one month, and remains high after three months (-0.94 standard deviations), but declines steadily after that, decreasing by two thirds (to -0.3 standard deviations) and becoming statistically insignificant by one year after the cash grant.

So, it is likely that there would need to be additional cash grants each year in order to keep these homeless people out of shelters. And note that these aren't the highest risk homeless people - they have only been homeless less than two years, and aren't suffering from serious mental health or substance abuse problems. On the other hand, perhaps this is the group that government really should be targeting, before they progress to serious mental health or substance abuse problems?

Anyway, a nice aspect of the study is that Dwyer et al. then went on to explore the beliefs and biases of the general population in relation to homelessness, finding that there is:

...a public mistrust of individuals experiencing homelessness in their ability to manage money. This mistrust can be a barrier for establishing cash transfers as a homelessness reduction policy.

However, when the general public is given information demonstrating the research results that homeless people do not spend the cash grant on 'temptation goods' (like alcohol, drugs, or cigarettes), or information about the cost-effectiveness of the cash grant, then people were more likely to support the cash grant policy. That was my experience in reading the research as well - the cost-effectiveness results were the results that most caught my attention. Dwyer et al. concludes that:

These two messages can be used to boost public support for a cash transfer policy to reduce homelessness.

Overall, this seems like a promising approach that is worth trying in other areas, including New Zealand. And importantly, Dwyer et al. have shown that it may be possible to get public buy-in to cash grants as a potential solution to homelessness.

The minimum wage and homelessness

The minimum wage has a number of effects, both inside and outside of the labour market (see the links at the end of this post for more). Low-income workers (such as many of those working for the minimum wage) are particularly vulnerable to any negative effects it may have. For that reason, I was really interested to read this recent working paper by Seth Hill (University of California San Diego), on the relationship between minimum wages and homelessness.

There are a couple of reasons to expect that higher minimum wages might increase homelessness. If minimum wages decrease employment (a result that is contested, but I believe it is likely given the galaxy of literature we have to date; again, see the links at the end of this post), then higher minimum wages may directly increase the risk of people becoming homeless. That's because when low-income people workers lose their jobs, they may no longer be able to afford to pay rent, and may lose their homes. Second, if minimum wages increase incomes for those that are not made unemployed, they may increase the demand for housing, pushing up rents. This may indirectly increase the risk of people becoming homeless, who can no longer afford the higher market rent.

Hill looked at municipality-level changes in minimum wages in the U.S., coupled with municipality-level data on homelessness from the Department of Housing and Urban Development's Annual Homeless Assessment Reports (which provides data on the number of homeless in each municipality in January each year). Hill used multiple different estimation methods, including: (1) an event study design; (2) a stacked regression estimator; and (3) a local projections difference-in-differences (DiD) estimator. For our purposes, we don't need to get too much into the details about the different methods, since they all point in the same direction. Hill finds that, in the event study analysis:

Municipalities that increased minimum wages by up to $2.50 per hour from 2013 to 2018 saw an average increase of 14 percent in homeless counts in the years 2014 to 2019 relative to municipalities with no nominal change in the minimum wage (real decline) or with changes pegged to inflation (real no change). Municipalities that increased minimum wages by more than $2.50 per hour from 2013 to 2018 saw an average increase of 23 percent in homeless counts in the years 2014 to 2019 relative to municipalities with no change. A dynamic version of this analysis suggests the increase in homeless counts increases as time passes.

For the stacked regression estimator:

Increases of $0.75 or more in local minimums increased relative homeless counts by about 25 percent in the years following the increase. All results hold with controls for changes in local income and local population.

And finally, for the local projections DiD estimator:

...when cities raise their minimum wage by 10%, relative homeless counts increase by three to four percent.

Overall, all of the results suggest that when the minimum wage increases, homelessness increases. Hill then goes on to consider the mechanisms that might explain this relationship, and finds that:

Using the event-study estimator to evaluate mechanisms, I find that increases in the minimum wage decreased employment among low-skill workers and increased costs of local rental housing in my sample.

That seems to support the theoretical direct and indirect effects of minimum wages on homelessness that I outlined at the start of the post. The minimum wage may make some workers better off, but it has unintended consequences. Here is another consequence that policy makers and others need to take account of.

[HT: Marginal Revolution]

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data

There should be no debate at all about rent controls

Rent controls have a number of negative effects. They lead to excess demand for housing, which is worse in the long run than the short run. They create a deadweight loss (a loss of economic welfare overall). They reduce the quality of rental housing (to the extent that rent controls have deadly consequences), and increase the quantity of vacant housing. They may even increase inequality (see here and here). In fact, the Swedish economist Assar Lindbeck (who passed away in 2020) was quoted as saying:

“Rent control appears to be the most efficient technique presently known to destroy a city—except for bombing.”

And yet, despite the overwhelming evidence of the negative effects of rent controls, people still advocate for them. Or, they or argue that we need to re-examine them based on flimsy reasoning. For example, in this article in The Conversation, Tom Baker (University of Auckland) asks us to have an open mind about rent controls. An open mind couldn't fail to see that the evidence is strongly against rent controls as a way of helping low-income tenants. We don't need to rely on an economic model for this - the empirical evidence (in the posts linked above) supports it.

Fortunately, not everyone has starry-eyed views of rent controls and is unwilling to consider the weight of the evidence. This article in The Conversation by Ameeta Jain (Deakin University) concludes that:

While freezing rents would appear to be a simple method to increase rental housing affordability, the unintended consequences of any such move will have a long-term negative impact on the total availability of rental housing stock, reducing the quality of housing and increasing a black market in rental housing.

Global experience suggests that improving supply, by easing building restrictions and scrapping red tape for new developments, is likely to be a more effective policy tool in Australia.

As for helping low-income tenants, I said it best in this post in 2015:

This excess demand can have a range of negative effects, depending on how it is managed. Perhaps the excess demand is managed by waiting lists of various flavours (as in Stockholm or Copenhagen), which means that potential tenants have to wait years for a rent-controlled space to become available. Instead, perhaps landlords are left to manage the excess demand on their own, in which case the rent-controlled housing is more likely to be rented to higher income tenants. Why? The landlord has a lot of choice over tenants now (because of the excess demand). If they can choose to rent their house to the professional couple with two incomes, or the solo mother with no job and three young children, it doesn’t take an economics PhD to work out who is going to miss out. So in this case the rent control actually hurts the very people (low income tenants) that it was designed to help.

On top of that, landlords might be willing to accept side-payments (bribes) to ensure access to rental housing. Tenants are willing to pay the bribes to ensure they don't miss out on a place to live. This further stacks the rental market against low-income tenants.

The very tenants that rent controls are designed to help, end up being the tenants that are most hurt by the policy. If we are worried about low-income tenants, perhaps we should do something about their low income, or do something that raises supply of rental property (which would increase competition among landlords and reduce the equilibrium rent). Rent controls are a policy failure on so many dimensions and are best forgotten.

Read more:

• Why rent controls have worse effects over longer time horizons
• The deadweight loss of rent control
• The deadly consequences of rent control in Mumbai
• Rent control and inequality in San Francisco
• Rent control and the redistribution of wealth
• Rent control and vacant properties in India
• Rent control according to Seinfeld

Unnatural deaths and apartment prices

If you were looking to buy a house, and you found out that someone had recently died an unnatural death in the house, would that affect what you are willing to pay for the house? I suspect it would for many people, being a negative characteristic of the house. Based on hedonic demand theory, which my ECONS102 class briefly covered this week, negative characteristics reduce the overall price of the good. 

How does that work? Hedonic demand theory recognises that when you buy some (or most?) goods you aren't so much buying a single item but really a bundle of characteristics, and each of those characteristics has value. The value of the whole product is the sum of the value of the characteristics that make it up. For example, when you buy a house, you are buying its characteristics (number of bedrooms, number of bathrooms, floor area, land area, location, etc.). You are also buying the characteristic of whether the house has had a recent unnatural death, or not. When you bundle all of the characteristics' values together, you get the value of the house.

So, does an unnatural death really reduce house prices? That is the research question addressed in this 2018 article by Zheng Chang (City University of Hong Kong) and Jing Li (Singapore Management University), published in the journal Regional Science and Urban Economics (ungated earlier version here). They use housing unit (mostly apartments, I guess) sales data from Hong Kong housing estates between 2001 and 2015, and first note that:

Influenced by Taoism, traditional Chinese believe that people who died as a result of violence or unnatural events can become “ghosts” who can disturb successive occupants through various means... Housing units in which unnatural deaths have occurred are called “haunted units,” which are regarded as bad Feng Shui, and are unsuitable for habitation...

So, the expectation is that a recent unnatural death will reduce house prices. Comparing houses with and without a recent unnatural death (and controlling for housing unit characteristics), Chang and Li find that:

...housing values drop about 25% for units with deaths, 4.5% for other units on the same floor, 2.6% for other floor units in the same building, and 1% for units in other buildings of the same estate. However, the average house price of units in other estates within 300m increases 0.5%. For units with deaths, the price decline is sustained across the whole study period. The price impact on units in other geographic scopes follows a U shape and starts to reverse after 4–5 years of a death.

In other words, there is evidence for a sustained negative impact of an unnatural death on housing unit prices, as well as a shorter-term impact on surrounding housing units. Even having a housing unit on the same floor, or in the same building, as the unit where there was a recent unnatural death, is enough to lower the housing unit's price. Sometimes, superstition matters for consumer preferences.

Is there not a compensating differential for having a naked landlord?

The New Zealand Herald reported earlier this week:

A landlord sunbathing naked in the courtyard of his building was no reason for one of his tenants to withhold its rental payments, a German court has ruled.

The case involved a building in an upmarket district of Frankfurt, which included an office floor, rented by a human resources company. The company withheld rent because it objected, among other things, to the landlord’s naked sunbathing. In response, the landlord sued.

The Frankfurt state court rejected the company’s reasoning, finding that “the usability of the rented property was not impaired by the plaintiff sunning himself naked in the courtyard”.

It said in a statement that it could not see an “inadmissible, deliberately improper effect on the property”.

In labour markets, economists refer to the idea of a compensating differential. This recognises that jobs with different non-monetary characteristics will have different equilibrium wages. Jobs with desirable characteristics will attract more workers (higher labour supply) and have lower wages. Jobs with undesirable characteristics will attract fewer workers (lower labour supply) and have higher wages. The difference in wages between otherwise identical jobs, based on their non-monetary characteristics, is a compensating differential.

Should there be a compensating differential for having a naked landlord? Unless your landlord is Henry Cavill, it is likely that having your landlord sunbathing naked around your building is likely to be an undesirable characteristic of your apartment. [*] The rent should be lower for the tenants of a naked-sunbathing landlord, to compensate them for that negative non-monetary characteristic.

However, having said that, it shouldn't be up to the courts to enforce a lower rent (by allowing a tenant to withhold rent payments). A landlord with a penchant for naked sunbathing would probably find that they have to find new tenants more often than other landlords, necessitating a costly search process (and an empty apartment in the meantime). That would lead the average rent from the apartment to reduce. So, unfortunately, unless a tenant can negotiate with the landlord about the rent (and given the experience of the tenant noted in the article, that seems unlikely), the compensating differential likely only affects the landlord, and not the tenants.

If the landlord is willing to receive a lower rent on average, then it appears that they can sunbathe naked with impunity.

*****

[*] For the tenants of a naked-sunbathing Henry Cavill, this would probably be a desirable characteristic. Their rent would likely be higher as a result.

Rent control according to Seinfeld

My ECONS101 lecture today covered price controls, and as examples we discussed the minimum wage (as an example of a price floor) and rent control (as an example of a price ceiling). On the topic of rent control, I was really interested to read this new article by Shane Sanders (Syracuse University), Andrew Luccasen (Mississippi University for Women), and Abhinav Alakshendra (University of Florida), published in the American Journal of Economics and Sociology (open access). They outline a number of useful examples where the 1990s TV show Seinfeld can be used in teaching rent control from an economic perspective:

More than 30 years after its premiere, Seinfeld continues its run as a seminally popular television show. Set in New York City, where rent control laws have a long history, a recurring theme of the show concerns the trials of apartment living. In several episodes of the show, characters must deal with the difficulty of procuring an apartment in a city with rent control or rent stabilization policies (shortage, tastes for discrimination by seller, bribery, and search costs), as well as the difficulty of maintaining the quality of a rent-controlled apartment over time once one has been procured (quality degradation). Seinfeld also illustrates the informal process through which rent-controlled apartments are advertised, and that less advertising takes place under rent control induced shortages.

The specific episodes that Sanders et al. outline are The Robbery (Season 1, Episode 3), The Apartment (Season 2, Episode 5), The Shower Head (Season 7, Episode 16), and The Andrea Doria (Season 8, Episode 10). The cool thing about these episodes is that they illustrate many of the negative consequences of rent control. As Sanders et al. note:

In The Andrea Doria, we discuss seller discrimination and bribery as two potential consequences of a rent control policy. The Apartment revisits the theme of bribery and also discusses advertising in the case of underprovision. The episodes The Shower Head and The Robbery illustrate the negative effect of rent control upon housing quality.

As rent control leads to excess demand for apartments (a shortage), many would-be tenants miss out on apartments. That allows landlords to discriminate, because they have a lot of choice over who to rent their apartments to. In my class, I noted that low-income tenants would likely be among those to miss out on rent-controlled housing, because landlords would prefer to rent to high-income tenants instead. Rent controls also provide an incentive for tenants to use side payments (for example, bribes) to ensure that they can secure a rent-controlled apartment. Rent controls also change the incentives for landlords. Since there is no shortage of tenants looking for an apartment, landlords can afford to skimp on maintenance of their apartments, lowering the overall quality of housing. Landlords can also afford to avoid the cost advertising when they have an apartment available, because they can rely on word-of-mouth instead.

Sanders et al. have done a great job of collating these examples. The sad thing is that each example relies on multiple clips from the episode, and as far as I can see, those clips are not available on the official Seinfeld YouTube channel. I guess you could rely on this site (which streams Seinfeld episodes non-stop), but you'd need some way of recording them. Or, you have to buy the Seinfeld DVDs. Or watch Comedy Central, which has been spamming Seinfeld episodes in the evenings for the last couple of months.

On the plus side, they reminded me that there is a whole website devoted to the economics on Seinfeld (and a book!). If you love Seinfeld, there is a lot to learn about economics from this show.

Read more:

• Why rent controls have worse effects over longer time horizons
• The deadweight loss of rent control
• The deadly consequences of rent control in Mumbai
• Rent control and inequality in San Francisco
• Rent control and the redistribution of wealth
• Rent control and vacant properties in India

Rent control and vacant properties in India

Across the street from my home is a vacant house. It's been vacant since at least mid-2019. In the middle of a housing crisis, the house remains vacant. Various people in the neighbourhood have wondered why the owner doesn't rent the property out. It made one of our neighbours incredibly angry. They wanted to buy a house (in 2019), but they couldn't find that was affordable. And yet, the house next to their rented home was vacant.

Why is the house vacant? Why won't the owner rent just it out? If you look at it, you realise that there are a lot of impediments to becoming a landlord. On 1 July 2019 (around about the time that the house was vacated by its owner), the government introduced new 'healthy homes' standards, that all rental properties would eventually need to meet. The house would need to be insulated, and meet heating and ventilation standards, along with some other conditions. If that would require expensive upgrading of the house (and that seems entirely plausible), then the landlord might have decided it would not be worth the hassle, and has since kept the property vacant. [*]

The healthy homes standards are not the worst policy the government could have enacted that would have led to vacant houses. Thankfully they have never followed through on early indications that they were considering rent controls. It is well known (to economists, at least) that rent controls lead to a worsening of the quality of rental housing (to the extent that rent controlled housing is literally killing people in Mumbai). But rent controls also increase the number of vacant houses.

A good examination of why vacancy rates are higher when rent controls are in place was provided by this recent article, by Sahil Gandhi (University of Manchester), Richard Green (University of Southern California), and Shaonlee Patranabis (London School of Economics), published in the Journal of Urban Economics (open access). Gandhi hypothesise that rent controls and lack of state capacity for legal enforcement of contracts both reduce the security of property rights, and that leads landlords to leave their properties vacant:

Two phenomena could create uncertainty in this allocation of rights of ownership between the landlord and the tenant. First, rent control, whose aim is to protect tenants from rent increases and evictions, alters the allocation of ownership in favor of the tenant. Second, if courts take long to resolve disputes, the ownership of the property could de-facto belong to the tenant for this duration and thus increase the risks for the landlord... The presence of either of these two conditions reduces ex-ante incentives for the landlord to engage in a rental contract. High vacancy rates are a natural consequence of reducing the benefits and raising the costs to a landlord of renting.

The problem of vacancies is particularly acute in India, where:

...the vacant stock of 11.1 million units could house almost 50 million people or around 13% of the urban Indian population.

Gandhi et al. use district-level data from the 2001 and 2011 Indian Censuses, essentially comparing the proportion of vacant properties between districts with and without rent controls. They also look at the relationship between vacant properties and state capacity for contract enforcement, measured as the number of judges per 1000 people. They have panel data for 456 districts across 24 states (for rent control) and cross-sectional data for 580 districts across 29 states (for state capacity). In their analyses, they find that:

...a pro-landlord policy move that relaxes rent revisions could potentially reduce housing vacancy by 2.8 to 3.1 percentage points and lead to a net welfare gain...

...a one to two standard deviation increase in judges per 1000 persons (urban) could reduce vacancy by 0.43 to 0.86 percentage points...

In other words, both rent controls and a lack of state capacity for contract enforcement lead landlords to leave properties vacant rather than renting them out. Gandhi et al. conclude that:

...rent control reform and judicial capacity are two areas in need of urgent attention from policymakers. The Model Tenancy Act, approved in June 2021 by the Government of India, aims to address both issues. It allows for setting rents at market rates and requires separate fast track courts to resolve disputes between tenants and landlords. If states adopt this Act then our findings suggest that vacant housing will decline.

Note that introducing rent control, and making it more difficult for landlords to evict bad tenants, would tend to shift things in the opposite direction. Both are policies that the current New Zealand government has actively considered. The consequences are clear.

[HT: Eric Crampton at Offsetting Behaviour]

*****

[*] In the last two years, things have gotten even worse for the house. A pipe burst in 2020 and flooded underneath the house. The owner didn't do anything. A large silk tree in the front yard rotted, then finally collapsed. Still no sign of the owner. The house is virtually abandoned at this point. I suspect it is not only un-rentable (given the healthy homes standards), but is probably unsaleable as well.

Read more:

• Why rent controls have worse effects over longer time horizons
• The deadweight loss of rent control
• The deadly consequences of rent control in Mumbai
• Rent control and inequality in San Francisco
• Rent control and the redistribution of wealth

Retail marijuana stores and house prices

Does having nearby retail stores affect the value of homes? Surely it does, but just as surely it depends on the type of retail stores. If your neighbourhood has a lot of payday lenders, pawn shops, and discount liquor stores, that is quite different from a neighbourhood that has health stores, pet stores, and premium wine shops. So, one way of measuring whether a community prefers to have more (or less) of particular retail stores is to measure the effect on house prices. If, when a new retail store opens, local house prices increase, then the community believes that store is a good thing. If, on the other hand, local house prices decrease, then the community believes that the store is a bad thing.

This relies on hedonic demand theory (or hedonic pricing), which recognises that when you buy some (or most?) goods you aren't so much buying a single item but really a bundle of characteristics, and each of those characteristics has value. The value of the whole product is the sum of the value of the characteristics that make it up. For example, when you buy a house, you are buying its characteristics (number of bedrooms, number of bathrooms, floor area, land area, location, etc.). As part of the location, you are buying the fact that there are a number of retail stores of different types in the local neighbourhood. So, controlling for all of the other characteristics of houses, comparing the price of houses in areas with some types of retail stores with similar houses in neighbourhoods without those same types of retail stores, provides one way of determining how the community views those retail stores.

As part of my ongoing research on alcohol outlets, I have toyed with the idea of performing this analysis for house prices and alcohol outlet locations in New Zealand. However, there are a bunch of other outlet types that we may be interested in, like vape stores. And, if New Zealand ever gets around to legalising marijuana, it would be interesting to see the effect of retail marijuana stores on house prices.

That's more or less exactly what this 2020 article by James Conklin (University of Georgia), Moussa Diop (University of Wisconsin-Madison), and Herman Li (California State University), published in the journal Real Estate Economics (ungated earlier version here, and research brief here), did. Conklin et al. look at what happened to house prices in Denver when Colorado legalised retail marijuana on 1 January 2014. Interestingly, the Colorado policy change allowed existing medical marijuana stores to become retail stores. So, the analysis doesn't compare retail store with no store, so much as what happens when an existing medical marijuana store becomes a retail store. However, this is important, as:

...since only existing medical marijuana stores were allowed to conduct recreational sales, we avoid the potential endogeneity of store location. Given the opportunity, retail marijuana stores would likely choose to locate in certain areas based on neighborhood characteristics that would also affect house prices. However, since only existing medical stores were allowed to sell retail marijuana, the siting decision was made before implementation of [legalized recreational marijuana].

Conklin et al. use a difference-in-differences approach, comparing the difference in house prices (controlling for various house characteristics) between single-family houses within 0.1 miles of at least one retail marijuana store and single-family houses between 0.1 and 0.25 miles of at least one retail marijuana store, before and after the legalisation of retail marijuana. Their initial analysis, based on properties sold in 2013 (before the law change) and 2014 (after the law change) finds that:

...being located near at least one medical facility that converts to retail in 2014 is associated with an 8.4% increase in sale price after the retail conversion occurs.

You may be worried that the number of retail marijuana stores in the neighbourhood matter, but Conklin et al. also show that the results are similar for houses where exactly one store became a retail marijuana store in the local neighbourhood (in fact, a slightly larger 11.4% increase). The results are also robust to alternative definitions of the control groups, but expanding the treatment group to include houses further away from retail marijuana conversions reduces the effect (which should be no surprise - those houses were in the control group in the original analysis).

Overall, we can conclude that local communities in Denver like retail marijuana stores. Or, more correctly, local communities in Denver prefer retail marijuana stores to medical marijuana stores (since we don't know how they feel about the medical marijuana stores, and house prices went up after those stores convert to retail). This is consistent with other evidence (as I discussed in this 2018 post), showing that legalising marijuana increased house prices across all of Colorado (rather than just Denver) by about 6 percent. The downside of this, of course, is that if you are in favour of legalising marijuana in New Zealand, one trade-off may be that house prices go up even further than they already have.

Read more:

• Legalised marijuana sales and house prices

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.

Some notes on the NZ Government's new housing package

The big news in New Zealand this week was the announcement of the government's new housing package, which included:

• A $3.8 billion fund to accelerate house building;
• Extra support for first home buyers;
• An extension of the 'bright-line' test to ten years, capturing a greater proportion of house sales within what is effectively a capital gains tax;
• Removing tax deductibility of interest payments for residential landlords; and
• Additional support for apprentices.

The goal of the package is to "increase the supply of houses and remove incentives for speculators, to deliver a more sustainable housing market".

The housing market is complex, and the problems associated with high house prices and rents defy simple solutions. A package was always going to be necessary, but the various parts of the package need to work in concert. Let's take a quick look through those five bullet points and think about the effects they might have on the housing markets. I say markets (plural) because the effects may be different on the market for homes (for owner-occupiers and investors) and on the rental market.

First, $3.8 billion to accelerate house building sounds good on the surface. However, remember that the government isn't a builder of houses, and that's not what this fund is for. The $3.8 billion is to fund infrastructure such as roads and water. This will (hopefully) make it less costly for local councils to zone additional land for development, and for developers to develop the land, since presumably it means that the developers will get to pay lower development contributions. At least, that's how I assume it will work, and if that's the case, then the costs of development will fall, and that should reduce the costs of newly built houses. Some of that reduction in cost will be passed onto new home buyers in the form of lower prices. However, if there is no change in development contributions, then any effect on new house prices is likely to be a lot smaller.

Lower house prices for new builds should lower house prices for existing homes as well, because the two types of homes are substitutes - if more people are buying new builds, there will be less demand for existing homes. Lower house prices mean lower costs for landlords (because their mortgage payments, as well as potentially insurance and rates, will be lower).

Second, extra support for first home buyers is going to undo some of the price effects of the infrastructure fund. Increasing home grants (as noted here) "from $85,000 to $95,000 for individuals and from $130,000 to $150,000 for two or more buyers" is essentially increasing the size of the subsidy for home buyers. Subsidies tend to push up prices, because more buyers are going to be looking for homes, and all the effects on prices noted above will work in reverse.

Third, the extension of the 'bright-line' test to ten years will make a difference at the margin for some existing home owners. They will be more reluctant to sell within ten years, if their home has been rented out at any time. This will reduce some speculator demand in the house market, and act to reduce house prices. However, most speculators were probably being captured by the previous five-year bright-line test anyway.

Fourth, removing tax deductibility of interest payments for residential landlords possibly has the biggest effect, and is considered by many people to be the most consequential of the changes (e.g. see here). This change will reduce the 'profitability' of being a residential landlord. Investors will want to get out of the market, shifting some houses out of the rental sub-market and into the owner-occupier sub-market. This will reduce house prices, but increase residential rents because landlords will now need to cover more expenses across the year as they will be paying more tax (or, more likely, paying tax as opposed to offsetting rental losses against their other income, or carrying losses forward to future years).

Alternatively, some landlords might seek to shift their residential properties into the commercial market, especially if they are zoned in mixed-residential or commercial zones. If you rent a house out to a business to use as an office and/or workshop, it is likely that this is a commercial rental and interest would still be tax deductible. Similarly, landlords might shift their houses onto Bookabach or AirBnB, accelerating an existing trend. That is particularly likely as borders re-open and tourist flows resume. Again, that makes the house rental commercial rather than residential and interest would likely be tax deductible. It will be interesting to see what (if anything) the government tries to do to prevent this type of activity. However, to the extent that landlords move houses out of the residential and into the commercial or short-term accommodation markets, the supply of rental houses will reduce and residential rents will increase.

Finally, additional support for apprentices will only have a small effect on the housing market. More apprentices now isn't going to make much difference to the number of builders and tradesmen available now, but will do in the future.

Overall, what can we conclude? On balance, house prices are probably going to fall a little, but it depends on how much demand is stimulated by first home buyers, and how many landlords look to exit the market, rather than shifting their houses into commercial uses. Residential rents are almost certainly going to increase.

Who really benefits from this package? The government wanted to help first home buyers, and this package will likely succeed. Property developers also benefit (if the development contributions that they would usually be required to pay are reduced).

Who is paying the costs? The taxpayers is picking up some of the cost, including the increased support for home buyers and apprentices, and the $3.8 billion infrastructure package. Landlords are going to be worse off due to their higher tax liability, and many of them will no doubt be seriously considering exiting the market. However, the group that may be most hurt by this package will be low income renters. Families whose income is too low to be able to contemplate saving a house deposit are almost certainly going to be paying higher rents.

Of course, as I said earlier, the housing market is complex, and there is a lot going on. The local and global economies are recovering from the pandemic, interest rates are currently at all-time lows, and international migration (inward and outward) has slowed to a trickle. None of those situations are going to persist forever, and as they change they will also have effects on the housing market. It will be interesting to see how it all plays out.