Public Comment RE Rent Stabilization — Salinas City Council, August 20, 2024 (part 1 of 2)

October 23, 2023
Mayor and City Council
City of Salinas
200 Lincoln Ave
Salinas, CA 93901
Re: October 24, 2023 City Council Meeting - Item ID#23-648 - Rent Stabilization and Tenant Protection - OPPOSE
Dear Mayor and City Council:

The Salinas Valley Chamber of Commerce sent a letter to you dated February 28, 2023 entitled “Position on Rental Registry Ordinance Proposed by City of Salinas Staff.” It stated the following:

Under the leadership of Community Development Department staff, particularly Director Megan Hunter, the committee worked toward creating an ordinance with language that all parties could accept as a reasonable compromise. We believe an acceptable compromise has been achieved.

Our letter also stated the following:
…the Chamber recommends that the Mayor and City Council wait to see measurable, data-based results of this rental registry program before imposing additional fees and regulations on property owners who rent their properties.

Our position has not changed, and therefore we oppose this proposal to abandon the compromise and instead prepare “rent stabilization” and “tenant protection” ordinances. We also ask that you maintain the Technical Advisory Committee on Housing rather than assigning the Housing and Land Use Committee as the forum through which the City engages the community on these issues.

Sincerely,
Bill Hastie
Board Chair
attachment

February 28, 2023

Mayor and City Council
City of Salinas
200 Lincoln Ave.

Salinas, CA 93901

Re: Position on Rental Registry Ordinance Proposed by City of Salinas Staff

Dear Mayor and City Council:

In 2019, and again in 2022 and 2023, the City of Salinas Community Development Department convened a Technical Advisory Committee to discuss the development and implementation of a “rental registry” ordinance, as directed by the City Council. Thank you for the inclusion of the Salinas Valley Chamber of Commerce in that advisory committee.

This ordinance is a strategy for housing, not a solution in itself. The Chamber strongly encourages the Mayor and City Council to move beyond verbal commitments and make an aggressive effort to increase the supply of housing that ordinary people can afford.

While the City of Salinas is transparent about its progress with the Regional Housing Needs Allocation (RHNA) in the Housing Element of its General Plan - and has performed much better than many other Monterey County local governments - the achievements are still well short of the goals. Regrettably, the City Council (and the public) only gets to see these shortcomings once a year, when the city submits its mandatory data to the state. See the attached table.

Increasing the housing supply needs to move beyond state-funded permanent supportive housing and random, speculative proposals from private developers that come on infrequent occasions. It requires leadership and responsibility for seeing the transformation of the city’s blighted, underused, and vacant parcels into assets for the community.

The Chamber supports the hiring of an Economic Development Director who can act as a comprehensive thinker and visionary in that position. This person should be someone who reports directly to the City Manager, not a technocrat who fills out grant applications and government forms as a cog in a sprawling operation.

This person should be working with every city council member to identify the parcels in their districts that should be targeted for activation.

Also, the Mayor and City Council should be aware that the Chamber’s neutrality on these new Rental Registry fees and regulations is conditional. If the Chamber sees parties exploiting the registry for “shakedown” purposes or sees the rental registration fee revenue diverted for purposes unrelated to identifying and rectifying unsafe and substandard rental housing, we will present the evidence and demand repeal of the ordinance.

In addition, the Chamber recommends that the Mayor and City Council wait to see measurable, data-based results of this rental registry program before imposing additional fees and regulations on property owners who rent their properties.

The Chamber supports “a thriving, welcoming Salinas Valley where people, families and businesses succeed via economic growth and opportunity.” This includes accountability for property owners and justice for tenants. We hope this ordinance flushes out landlords in the City of Salinas who take advantage of tenants by providing them with unsafe and substandard housing conditions.

Sincerely,

Bill Hastie
Chairman of the Board of Directors

City of Salinas Regional Housing Needs Allocation (RHNA) Progress

2015-2023 Housing Element (5th Cycle)
Income Category RHNA
Goal 2015 2016 2017 2018 2019 2020 2021 2022 2023 Total Units to Date
Total
Remaining
Units
%
Achieved
Very Low 537 21 24 50 42 0 0 88 0 225 312 42%
Low 351 20 16 0 53 8 0 1 0 98 253 28%
Moderate 407 6 1 3 0 0 0 0 0 10 397 2%
Above Moderate 934 142 52 25 71 100 210 189 139 928 6 99% Total RHNA 2,229 189 93 78 166 108 210 278 139 1,261 882 57%

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From: Gloria Moore
To: "Christopher Callihan"; "Lisa Brinton"
Subject: Another Player in the Rent Capping Arena
Date: Monday, April 8, 2024 12:53:31 PM
Attachments: scan_20240408113004.pdf
TO ALL:

Here is another player in the rent capping arena.

I am expecting more players to keep arriving.

Gloria J. Moore
Advocate for Common Sense
From: Krystal Lazcano
To: Christopher Callihan
Cc: Araceli Jacinto
Subject: FW: Comments on the City of Salinas Draft Rent Stabilization Ordinance Date: Friday, August 2, 2024 4:13:34 PM
Note sure if you need to see this, it came through the legal web mail. -Krystal
-----Original Message-----
From: Alan Stumpf <[email protected]>
Sent: Friday, August 2, 2024 4:06 PM
To: _legalwebmail <[email protected]>
Cc: housingwebmail <[email protected]>; Larry Bussard <[email protected]>; Rhiyan Quiton <[email protected]>
Subject: Comments on the City of Salinas Draft Rent Stabilization Ordinance CAUTION: This message originated outside of the City of Salinas email system. Do not click on links or open attachments unless you are sure the content is safe.

Thank you for this opportunity to comment on the draft ordinance.

I am a long-time owner of a duplex rental (that
was formerly my residence) in an older Salinas neighborhood, and I am also a former City employee and former department head of the Redevelopment and Community Development Departments. Having reviewed the proposed rent stabilization ordinance and attended the the community meeting on August 1, I have a few questions that I felt were not adequately answered in the presentation. I also have strong suspicions that this ordinance would be a huge regulatory burden on the City, a cumbersome and expensive financial burden on a selected few landlords, and provide too-little benefit to low-income residents needing rent relief.

Further, ordinances of this type are very much
in the experimental phase and, in the long run, will likely have unforeseen, negative consequences for the City as a whole.

The first question involves how the determination was made for which rental units would be subject to the proposed ordinance, and those that would not.

Based on the presentation from EPS, slightly more than one third of rental units (and only one fifth of all housing units) would be subject to the Ordinance. This means that only the oldest rental units in the City would bear the burden of ordinance requirements, while the vast majority of rentals would not.

The 1995 cutoff date seems arbitrary, as does the exclusion for renting single family homes. Placing such a burden only on owners of the oldest units in the City thus seems arbitrary, capricious, and wholly against providing fair and equitable treatment of housing throughout the City.

The EPS analysis should further evaluate how many of
the units subject to the ordinance are actually charging very high rents; its analysis is only based on averages. As noted at the meeting, its analysis is also based on economic data and assumptions that are several years old and it did not create any original research data for the City.

Ultimately, the few low income families who will actually benefit from the ordinance will be like winners of a slow- evolving lottery, rewarded on the one hand, perhaps, with lower rent over time, but on the other hand, they will likely remain in units long past when, in an unobstructed market, would turn over as family living conditions change. This is indicated as a benefit in the EPS analysis.

However, some examples of how this could play out in
the long term - 1. A family moves into a 3 bedroom unit at a price affordable for them; over time the children grow up and move out, leaving more bedrooms empty. Despite being “overhoused”, the tenant will continue to stay because of the below-market rent.

The ordinance, in the long run, is not only an incentive for tenants to stay longer, but would also lead to less housing available for families in the community, as that tenant remains under-housed.

This has been observed in many of the older, rent-subsidized, multifamily developments in the City. 2. In my own case, rents for my two units are well below market, but they are also very old and often in need of capital repairs and maintenance.

While the ordinance allows for increased rents for such purposes, the ordinance's approval process to increase rent accordingly, is very cumbersome and will likely add significantly to costs and time to effect the improvements. This becomes a big disincentive to effect needed repairs and maintenance, and would likely delay those repairs (for many landlords) until they become health and safety issues. This leads to older neighborhoods having less landscaping maintained, more peeling paint, more missing shingles, more broken fences and pavement, and overall less investment in those older neighborhoods; in effect, leading to more blighted older neighborhoods.

This isn’t an overnight effect, but rather one that is akin to the”broken window syndrome", that slowly leads a neighborhood into disrepair and blight.

I do not deny that rents have become outrageously high in Salinas and some unscrupulous landlords have taken advantage of many low-income renters in our community.

Shame on them. However, the ordinance does not
address outrageous rents prior to 2024, and higher baseline rent will only continue for those landlords- and can only continue upward after the new ordinance.

Meanwhile, most rental units in the City -those built after 1995 and others exempt from the ordinance, will enjoy significantly higher rents each year and over time. This increases disparity and disinvestment in the older parts of the City (i.e. concentrated in and around the downtown, and parts of East and North Salinas} versus newer areas.

Is this what the City wants - to accelerate disinvestment in these older neighborhoods?
In summary, the ordinance seems “too little, too late” to benefit any but a very few, very lucky, renters, only some of whom may be low-income, while putting in place a very cumbersome and expensive, untested City-wide apparatus for regulation - expensive both for landlords and the City budget (unless adoption of higher fees, of course). The ordinance discourages private investment in older development and neighborhoods, and thus in the long run promotes blight in those neighborhoods.

I would recommend proceeding cautiously,
and waiting to see the longer term results of this type of ordinance in other cities of similar size and demographics - this ordinance is an experiment that the City of Salinas needs to get right - the first time, and currently there are too many unknown, unintended consequences, with benefits for too few low income families.

I know that the City has long suffered from an unaffordable housing market, and I encourage the City to increase investment in affordable housing developments with for-profit and non-profit partners, leveraging their expertise and financial resources.

The City's (and former Redevelopment Agency’s) partnering with nonprofits such as CHiSPA, Interim Inc. and the Housing Authority has resulted in development of thousands of permanently affordable rental units in the City. These provide guaranteed affordable rents specifically for low income families, and they also leverage City funding with state and federal programs to assist those developments and residents.

As for me, maybe adoption of this ordinance is a signal for a new family to come along and purchase my old duplex, that will likely result in my long-time tenants needing to move on and presumably face much higher rents elsewhere.

I don’t wish to disrupt their lives, but It’s that or be subject to increasingly burdensome regulation benefitting neither myself nor my tenants.

Sincerely,
Alan Stumpf
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From: Patricia Barajas
To: Christopher Callihan
Subject: Fw: Opposition to rent stablization ordinance
Date: Wednesday, August 7, 2024 10:20:01 AM
Patricia M. Barajas, CMC
City Clerk
City of Salinas
200 Lincoln Avenue
Salinas, California 93901
(831) 758-7383
From: susan wang <[email protected]>
Sent: Tuesday, August 6, 2024 10:26 PM
To: cclerk <[email protected]>; Mayor <[email protected]>; _district1 <[email protected]>; _district2 <[email protected]>; _district3 <[email protected]>; _district4 <[email protected]>; _district5 <[email protected]>; _district6 <[email protected]> Subject: Opposition to rent stablization ordinance

Dear all city council members:

My name is Susan Wang, I am writing as a house provider in Salinas to voice my objection against the proposed rent stabilization
ordinance in Salinas.

My husband and me are over 65 years old relying on rental income to support and cover the expenses of my and my husband's retirement living costs. We are hardworking people and have poured our life savings into investing in income property in Salinas, hoping that it will help to supplement our retirement income.

Since the pandemic, the cost to maintain properties has gone up tremendously due to inflation, supply chain issues and even insurance and utilities bills have gone up.

It is already hard to provide safe and decent housing for our tenants based on the current rent increase allowed by the State of California. If the city is going to adopt the rent stabilization ordinance in Salinas and limit rent increase to cap at 2.5-3%, it will leave house providers
with noreasonable return of investment, hence limiting
our ability to maintain safe and good housing environments for tenants.

Furthermore, this rent stabilization will limit the real estate potential in Salinas.

Developers or multiunit housing will cease to invest in Salinas. Investors will not want to invest and hence the overall real estate value of Salinas will drop tremendously. This will eventually impact the economy of the community and driving people, whether tenants or homeowners, to move out. Salinas will turn from a community to a dead city.

As taxpayers and house providers, I urge the city NOT to pass the rent stabilization ordinance.

Thanks so much!

From: GMRealtors
To: [email protected]
Cc: [email protected]
Subject: FW: RENT STABILIZATIO AND TENANT PROTECTION
Date: Monday, October 23, 2023 3:48:01 PM

Mayor & Council:

We all can agree that the statewide housing crisis is almost without a plausible solution. Attainable housing is at a current low with interest rates at a current high.

The market-place is under constraints that slow or eliminate new construction. Residents’ static wages are not rising to the current inflationary costs of goods and services.

In my 43-year association with Salinas’s real estate, the market place has under-valued the need for more rental housing - be it apartments or other styles of rental housing.

Short periods of housing growth get suppressed by long periods of housing growth mired in regulations and unreasonable demands that Inhibit growth in supply.

Vacancy rates within a marketplace are the controlling factor for setting rents. Supply v, Demand Available housing for sale at all levels determine the costs to purchase.

All this being affected by the regional marketplace.

The old planning practices of a local jobs/housing balance disappeared years ago when It quickly moved toward regional outcomes. Can this trend be reversed so that a city such as Salinas can actually build for our local needs?

What has happened in the renter/rental property balance? Local government should be encouraging local investments in a solid rental inventory. Our locally-owned, small-scaled landlords are being squeezed out by inflation, non-resident investors, and government interference in private property rights.

My point of view is that we all have become victims in this housing crises.

Thank you for allowing me time in your busy schedules.

Gloria J. Moore
Broker/Owner
GLORIA MOORE, REALTORS®

From: Patricia Barajas
To: Christopher Callihan
Subject: Fw: Rent Stabilization Ordinance
Date: Monday, August 12, 2024 9:28:59 AM
Attachments: Salinas City Letter Rent Cap .docx
Patricia M. Barajas, CMC
City Clerk
City of Salinas
200 Lincoln Avenue
Salinas, California 93901
(831) 758-7383
From: [email protected] <[email protected]> Sent: Sunday, August 11, 2024 10:55 PM
To: cclerk <[email protected]>; Mayor <[email protected]>; _district1 <[email protected]>; _district2 <[email protected]>; _district3 <[email protected]>; _district4 <[email protected]>; _district5 <[email protected]>; _district6 <[email protected]> Subject: Rent Stabilization Ordinance

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Sent from my iPhone
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From: Krystal Lazcano
To: Christopher Callihan
Subject: FW: Rent Stabilization Ordinance
Date: Friday, August 9, 2024 12:14:35 PM
Attachments: Rent Stabilization Ordinance_Letter_08.09.2024.pdf Here you go.

From: [email protected] <[email protected]>
Sent: Friday, August 9, 2024 11:59 AM
To:
housingwebmail <[email protected]>
Cc: _legalwebmail <[email protected]>; Rene Mendez <[email protected]> Subject: Rent Stabilization Ordinance

Messrs. Callahan and Mendez,

Please consider the attached letter regarding the proposed Rent Stabilization Ordinance.

Sincerely,
Greg Piini, MAI

_______________________
Piini Realty, Inc.
263 Lincoln Avenue
Salinas, CA 93901
Work: (831) 422-5327x101
Cell: (831) 596-5763
Email: [email protected]

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From: Patricia Barajas
To: Christopher Callihan
Subject: Fw: Rent Stabilization Ordinance
Date: Friday, August 9, 2024 1:26:39 PM
Patricia M. Barajas, CMC
City Clerk
City of Salinas
200 Lincoln Avenue
Salinas, California 93901
(831) 758-7383
From: Craig Coming <[email protected]>
Sent: Friday, August 9, 2024 12:25 PM
To: cclerk <[email protected]>; Mayor <[email protected]>; _district1 <[email protected]>; _district2 <[email protected]>; _district3 <[email protected]>; _district4 <[email protected]>; _district5 <[email protected]>; _district6 <[email protected]> Subject: Rent Stabilization Ordinance

Dear Representatives,
As one of the largest property managers in Salinas, I am very discouraged that at no point has anyone from the City of Salinas, the Mayors office, or anyone from City Council contacted me, or other property managers,
to learn and educate themselves on how landlords are doing in the current economy and rental market. If you would have, you would learn that the cost of running a rental property has drastically increased with inflation and other reasons. The costs to own rental properties are increasing at a dramatic rate. As you know, sewer rates are going up 122% from 2021 to 2026, water rates are skyrocketing, insurance rates have gone up 20- 30% in one year, etc.

We have maintenance vendors raising their rates because they are in high demand and cannot find employees to do the work. A water heater that cost $460 five years ago now cost $865-899. To maintain the properties at an acceptable level, owners are spending much more than they ever have. The operating costs have been increasing at higher rates than the rent increases. The other problem is that the buildings are aging and need major capital improvements. Most of the rental properties in Salinas were built over 50 years ago.

Now when we have a vacancy it is common to spend $15,000-$25,000 doing upgrades to the unit.

That mixed with new roofs, termite fumigations, new driveways, electrical panel upgrades, etc., the costs are higher than ever. We even need to fix the City Sidewalks when they fail.

Raising rents is a responsible way to maintain properties and improve the quality of our cities.

Do I think there have been some landlords that have raised rents irresponsible, yes. Do I care about the residents of Salinas, absolutely yes. Mangold Property Management has been managing apartments in Salinas for over 40 years and we feel like we have contributed to the quality of the neighborhoods where we manage. My goal has always been to provide the cleanest, safest, and best maintained rentals in Salinas. This is why we have convinced the owners we manage for to invest so much money into these properties. This ordinance will change the way we manage these properties. This is a real concern and the hundreds of owners I manage for feel attacked.

I believe you have the best intentions in mind. But the presentation on August 1st was misleading. The main factor is the current inflationary economy we have been in. None of the data that is being used is taking into consideration that there is a drastic increase in the cost to run these properties over the last 5 years and more particularly in the last 2 years.

In the
presentation they stated that “The empirical evidence dispels many of the concerns..” about the “Moderate” Rent Stabilization Ordinance.

A 2-3% cap on rent increases is not Moderate.

This would be one of the most restrictive ordinances in the country. This is a severe ordinance you are considering. The concerns that property values with go down, that it will reduce supply, reduce maintenance, and result in inflated rents are all real concerns, especially in our current economy.

They suggest a 65-85% of CPI rent increase cap. This is absurd. To consider an ordinance that does not even give a cost-of-living increase will bring all those concern to reality.

On the list of positives of the ordinance they listed Reduced demand for social services and an increase in discretionary income which helps boost local economy.

These positives are taking private ownership, turning it into subsidized housing, to apparently help the local economy.

This would only be fair if the government reimbursed the private landlords for the subsidy. Just like Section 8 properties.

You are considering an attack on landlords without looking at any other part of the equation of poverty and housing costs.

You are not considering a higher minimum wage, you are not talking about putting a cap on the expenses landlords incur, you are not talking with landlords at all.

I encourage any of you to reach out to me so that I can show you and educate you on the reality of what we are dealing with.

Any less would be irresponsible.

My door is open to discuss this any time.

Sincerely,

_________________________________
Craig Coming
President, Broker Lic#01258178
Mangold Property Management, Inc.
[email protected]
Office: (831) 372-1338 Ext. 107 Fax: (831) 372-1488
Notice of Confidentiality: This transmission constitutes an electronic communication within the meaning of the Electronic Commissions Privacy Act, 18 U.S .C. 2510, and its disclosure is strictly limited to the recipient intended by the sender of this message, together with any attachments.

This communication, including any attachments, may contain confidential and privileged material for the sole use of the intended individual or entity, and receipt by any party other than the intended recipient does not constitute a loss of the confidential or privileged nature of the communication.

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From: Krystal Lazcano
To: Christopher Callihan
Subject: FW: rent stabilization ordinance
Date: Monday, August 12, 2024 8:23:03 AM

From: Jeff LaTourette <[email protected]>
Sent: Saturday, August 10, 2024 3:58 PM
To:
_legalwebmail <[email protected]>
Cc: Tony Lombardo <[email protected]>
Subject: rent stabilization ordinance

Hello, I am sending in comments on the proposed rent stabilization ordinance. Below are my comments.

1. CEQA requires evaluation and mitigation of impacts to historic resources. There would be significant impacts to historic apartments in
Salinas. This ordinance will limit the resources for property owners to maintain the historic resource. Historic resources should be exempt from the ordinance.

2. Section 17.02.04 limit on rental rate increases. This amount needs to be set at a limit that will allow property owners the ability to maintain and operate their properties.

With rising costs such as insurance, material and labor for repairs and general maintenance it should be at least 5%. And for older buildings it should be even more as older building require more maintenance and repairs.

3.sec 17.02.07 (c) landlord may not charge for utilities unless separately metered.

This is in the landlord petition for rent increase section.

Many older units do not have
the ability to separately meter utilities. It is not equitable to allow units who have separate meters to charge and units (mostly older) who don't not to charge. Many landlords do not include the master metered utilities in the rent and bill separately so each tenant pays their fair share of the utilities.

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From: Patricia Barajas
To: Christopher Callihan
Subject: Fw: Rental Regulations proposed
Date: Thursday, August 1, 2024 10:30:15 AM
Patricia M. Barajas, CMC
City Clerk
City of Salinas
200 Lincoln Avenue
Salinas, California 93901
(831) 758-7383
From: Pete Deoudes <[email protected]>
Sent: Wednesday, July 31, 2024 5:15 PM
To: _district3 <[email protected]>; cclerk <[email protected]> Subject: Rental Regulations proposed

Hello Mr. Valenzuela,
I was recently sent the following notice:
"Update: Salinas Rent Stabilization Ordinance
The exact details are not finalized yet, but it is likely that the City of Salinas will pass the Rent Stabilization and Tenant Protection ordinance on August 20. This will probably include a cap on rent increases of 2-3% per year, with no adjustment for CPI." As a long time owner, with a partner, of 10 rental units in Salinas I cannot understand the logic or constitutionality of this proposal. Whoever did propose this has no understanding of what is involved in successfully maintaining rental units. Margins are tight enough as it is, and now we are expected to accept returns, not only below market rates, but well below the average rate of inflation the last 4 years? Not to mention the cost of insurance, materials, and service providers...unless those prices will be limited by the city of Salinas to 2-3% per year as well?
I guarantee that this action will result in a certain decline in rental quality as owners cannot afford to maintain their properties with such an artificially low return. Multi-unit property values will decline based on artificially limited income, resulting in less property tax revenue to the city, tenants will suffer with rentals that are not maintained due to lack of funds, and the availability of units will lessen by lowering the incentive for development of additional units.

The city has never shared in the tremendous risk that private citizens took to invest in rental housing.

Most of us exposed ourselves to financial ruin to take a chance and become income property owners.

Now the city is expecting private citizens to subsidize public housing by forcing us to operate professionally maintained rentals at a financial loss?

Such an ordinance may "sound good" to renters, but will actually make their situation worse for the reasons I have stated, but that does not appear to be a concern for most politicians in recent years. Just as the "fast food minimum wage law" has resulted in more automation and thousands of workers losing their jobs, pandering measures by politicians have driven a record number of businesses and people out of state. Having been born in Salinas, I am saddened to see the city follow down the same anti-business, and counterproductive path. Perhaps you, or members of your family operate a business. I would suggest you ask them how the city, arbitrarily and severely restricting what they could charge for whatever they produce, would impact them.

Pete Deoudes

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From: Hap Albers
To: [email protected]
Cc: Hap Albers
Subject: Fw: Salinas Rent Control
Date: Monday, April 29, 2024 9:02:32 PM
Hello Salinas City Government Representatives:

Rent control is a concept that sounds great and will definitely help you get reelected. It also has many unintended consequences that will ultimately hurt the City of Salinas, as it has San Francisco, Berkeley, and Oakland.

My background is apartment building sales since 1998. I have sold 200+ buildings, including 37 complexes in Salinas. Prior to selling commercial real estate, I worked as a sales representative in San Francisco for Old Republic Title Company - I was able to see the non-tourist areas and the many dilapidated apartment complexes. I have sold apartment complexes in Oakland and there are a lot of dilapidated apartment complexes in Oakland. San Francisco and Oakland have similar rent control to what is being proposed for Salinas.

What will happen with rent control in Salinas:

Tenant side:

1. Tenants will have locked in rent increases at whatever percentage the city council decides is equitable and fair.

2. The housing supply will not increase because there is no longer an incentive to construct new housing, including ADU's, redevelopment, and perhaps, the many parcels in the northern section of the City. One of my customers has decided to not construct ADU's on his property because of this ordinance.

3. As the building and neighborhood quality declines, the bad violence from the past could return. I pray this does not happen. This scenario is not needed in Salinas - it already has a bad reputation.

4. If the Costa Hawkins ballot measure succeeds and the City applies rent control to homes, then there will be many rental homes sold to owner occupants, further constricting the rental supply.

Landlord side:

1. Most apartment complexes have expense ratios which use 35%-45%+ of the collected income for operating expenses. Mortgage loan debt can add another 50%-65% leaving very little profit on a large down payment investment. These are not lucrative investments and come with constant repair issues, people issues, and legal risk from property management.

The owner has purchased a second job.

Most multi-family owners are experiencing a sharp cost increases due to insurance, insurance required repairs for roofs, electrical panels, sewer line replacement, water line replacement, all building materials, labor costs,
and the simple fact that most apartment complexes were
constructed from 1950-1980 - they are now old buildings. It flat out costs a lot more to operate an apartment complex today than it did two or three years ago and it will continue to increase because of the repairs previously mentioned.

One of my customers' insurance premiums went from $12,000 to $55,000 for a lot less coverage - just fire insurance only - no liability insurance for trip and fall down the stairs. 450% increase. This is not uncommon.

Another customer is being forced to replace roofs or lose insurance coverage.

Another customer is being forced to replace all electric panels - main and subpanels.

This is very common.

2. The owners will not be able to increase rents at inflation or greater. This results in lower revenue and less money to cover repairs, insurance, debt service, and other expenses.

3. Eventually, the owner will begin to let the building quality decline because their expenses will grow faster than rents.

4. Then they get in trouble with code enforcement but have less money to spend.

5. They also will have to get on their knees to plead for rent increases to cover capital improvements after
they are completed. Good luck with that

6. Maybe they will sell the property. The new buyer will have the same problem - rents have to grow at inflation or greater to cover expense inflation. Very simple. They will also have higher property taxes.

7. The eventual result is building quality will decline, they will sell for less, and the property taxes, which I believe the City of Salinas benefits from at some level, will not increase as rapidly as many in the budget
department are planning.

You should also expect the property owners to petition the county tax assessor for a property tax reduction.

8. For the owner, the day this rent control ordinance passes at less than 5% annual rent increases, the entire City of Salinas multi-family inventory value drops 20% plus or roughly $40,000 to $50,000 per unit.

9. Back to Costa Hawkins: If the ballot measure succeeds, then the owner's financial math gets worse if rents are not allowed to be increased to market rates when a voluntary vacancy occurs. Values drop further for every
California apartment complex if
Costa Hawkins is rescinded and there will be a lot fewer constructed, regardless of any rent control exemption window. This is a bad ballot measure.

Going forward: Rent control is easy to pass. Is it smart to do so is very questionable. If you do pass it, the annual rent increase minimum should be no less than the Bay Area CPI index plus 1 to 2%. San Jose has 5% annual
rent increases. This is fair to both parties.

The scenarios in the tenant side are all very possible outcomes. There will be unintended consequences - there always are when the government tries to engineer solutions to housing. The problem is supply and
solutions need to be focused on more
housing and rewarding those with the financial courage to provide it.

Thank you for taking the time to read this email. I would enjoy the opportunity to talk with you folks regarding rent control and how to create more housing in Salinas and the Central Coast.

Good afternoon to all. To all property owners who own rentals "you are under attack". You were just required to pay a fee to become a target "RENTAL REGISTRY". read the attached City of Salinas meeting agenda.

It is time for us as concerned citizens to form an association for self protection. The City of Salinas makes it difficult to build new houses and the more they attack property owners the fewer rental properties they will have. I was told by a person working in politics, that the general public would not come together to protect them selves from Government even poor Government. Wake up and smell the coffee.

Please respond to this email if you are interested in being free, and limiting government authority.

Chuck

The Government has many valuable services, RULING the public is not one of them.

From: [email protected]
To: [email protected]
Sent: 10/22/2023 5:09:20 PM Pacific Daylight Time
Subject: Fwd Rent Control by a different name
Preview of: 20231022165002965.pdf

Please respond to this email if you are interested in being free, and limiting government authority.

Chuck

The Government has many valuable services, RULING the public is not one of them.

From: [email protected]
To: [email protected]
Sent: 10/22/2023 5:09:20 PM Pacific Daylight Time
Subject: Fwd Rent Control by a different name
Preview of: 20231022165002965.pdf

CAUTION: This message originated outside of the City of Salinas email system. Do not click on links or open attachments unless you are sure the content is safe.

From: Patricia Barajas
To: Christopher Callihan
Subject: Fwd: What Rent Control will do to Salinas?
Date: Tuesday, August 13, 2024 9:37:50 AM
Get Outlook for iOS
From: Hui Qian <[email protected]>
Sent: Tuesday, August 13, 2024 5:55:43 AM
To: cclerk <[email protected]>; Mayor <[email protected]>; _district1 <[email protected]>; _district2 <[email protected]>; _district3 <[email protected]>; _district4 <[email protected]>; _district5 <[email protected]>; _district6 <[email protected]> Subject: What Rent Control will do to Salinas?

Swedish economist Assar Lindbeck famously said, “
rent control appears to be the most
efficient technique presently known to destroy a city — except for bombing.” Look at San Francisco! Look at Oakland! What happened there after they put in rent control? I am a retired senior citizen and have poured my life savings into investing in income property in Salinas, hoping that it will help to supplement my retirement income. Since the pandemic, the cost to maintain properties has gone up tremendously due to inflation, supply chain issues and even
utility bills have gone up a lot. The reality is:

It is already hard to provide safe
and decent housing for our tenants based on the current rent increase allowed by the State of California.

The city is going to cap rent increase at 2.5-3%, while the insurance cost has more than doubled since the pandemic, and it is still increasing in double digits, what would you do in this situation?
This rent stabilization will limit the real estate potential in Salinas. Developers or multiunit housing will cease to invest in Salinas. Investors will not want to invest and hence the overall real estate value of Salinas will drop tremendously. This will eventually impact the economy of the community and driving people, whether tenants or homeowners, to move out. Salinas will turn from a community to a dead city.

As a taxpayer and a housing provider, I urge the city to reject the rent stabilization ordinance.

From: Todd Dudley
To: [email protected]
Subject: Homeowner Input for Record for Salinas City Council Meeting of 24 Oct 23 // Opposition to Rent Control Date: Monday, October 23, 2023 5:35:48 PM
Dear City Council Members,
I am a California native and homeowner-landlord in Salinas and am writing to ask you to OPPOSE rent control in our beautiful city.

Inflation: Causes the Housing Problem. As you are aware, the high interest rates seen in the mortgage market today are a direct result of government spending, which increases M1 money supply in economy and causes prices to rise.

To counter the effect of government
spending, the Fed then increases the fed funding rate, which pulls this same money out of supply by raising interest rates (to include mortgage rates offered by banks to prospective homeowners).

Due the high interest rates present while trying to curb inflation, government spending thus significantly contributes to the lack of affordable housing at a time when supply of rental properties is already limited.

Imposing further rent controls during already-high
inflation (and consequent high rental demand) thus destroys any remaining market incentive available that would serve to increase the supply of rental homes.

Price controls will make the
housing problem worse; government spending needs to be curtailed.

Rent Control: Accelerates the Problem. While rent control sounds like a humanitarian solution to the above problem, it attacks and in fact punishes the wrong elements that contribute to an affordable housing market. In fact, rent controls will make the housing crisis worse, as they destroy any remaining market incentive available that would serve to increase the supply of rental homes. For people that already cannot contemplate the purchase of a home in Salinas, the rental market worsens in the short term and then completely evaporates in the long term.

Rent control will accelerate the housing crisis, a crisis best solved by less government spending, resulting lower interest rates, and more lending/construction/economic activity in the private sector.

Bottom line:

High Government Spending Causing Inflation, Plus Government Rent Control to Fix It = Long Term Damage to Salinas' Housing Market, and to our Beautiful City.

Thank you,
Todd Dudley
Homeowner
19386 Acclaim Dr
Salinas, CA 93908
CAUTION: This message originated outside of the City of Salinas email system. Do not click on links or open attachments unless you are sure the content is safe.

From: Vanessa Nguyen
To: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] Date: Sunday, April 7, 2024 11:29:20 AM
Here some of the key concerns I have regarding this ordinance:

1. Decrease in housing stock due to landlords' inability to navigate new regulations or afford housing provision.

2. Annual rental increases burdening tenants.

3. Lack of understanding regarding the high costs of property maintenance.

4. Challenges in recovering funds from tenants who damage property.

5. Potential for a housing crisis similar to that experienced in certain European cities. I believe that constructive dialogue and collaborative efforts are essential in addressing these concerns.

Therefore, I propose the following solutions for consideration:

1. Formation of small groups comprising landlords, property managers, builders, and tenants to discuss the issue.

2. Streamlining the housing development process by reducing regulatory barriers and associated costs.

3. Regulate the Monterey County Housing annual increases. Most property managers and landlords mirror their rates to them

4. Temporary rent subsidies to alleviate immediate financial burdens on tenants.

5. Exploration of municipal low-income housing initiatives.

6. Streamlined processes for converting vacant commercial spaces into housing units.

7. Provision of interruption services through the City to address disputes and for leases.

8. Enforcement of existing state rent control laws by mirroring them at the municipal level.

9. Ensuring equitable distribution of responsibilities between landlords and tenants.

10. Establishment of a task force to address issues related to substandard housing while supporting responsible landlords.

Monterey County Association of Realtors®
5 Harris Court, Building A
Monterey CA, 93940
October 24, 2023
Mayor Kimbley Craig
Councilmember Tony Barrera
Councilmember Carla Viviana González
Councilmember Steve McShane
Councilmember Orlando Osornio
Councilmember Anthony Rocha
Councilmember Andrew Sandoval
Re. Agenda item 23-648: Rent Stabilization and Tenant Protection Honorable Mayor and City Councilmembers,
Now is the time to seek collaborative, innovative, and pro-housing solutions to our housing availability and affordability crisis. This is especially true for rental housing; renters are in need to help and we agree steps must be taken to do so. However, policies that divide our community, punish housing providers, and disincentivize growth in housing will achieve the opposite.

Consistent with other recent changes to City housing policies, we request that these policy proposals:

1. Be referred to the Technical Advisory Committee on housing for expert input including tenants right advocates, and..

2. That no fewer than six (6, one in each district) public meetings , hearings, or workshops be held to gather community input.

Enhance tenant protections were recently signed into law by Governor Gavin Newsom, and go into effect in January. Changes to how State and local government administer rental pricing regulations are expected to be on the November 2024 ballot. In light of these upcoming or

anticipated changes to State policy, it does not make sense to pursue local policies which may turn out to be redundant, contradictory to, or otherwise in conflict with State law.

Additionally, the City’s rental registry program is incomplete. Earlier this year members of the council supporting the registry expressed a desire to see data from the registry in order to better inform further policy decisions. The registry has just barely been implemented, there has been minimal communication from the City to property owners about this requirement. We were promised an online portal where property owners and managers could log in and manage and update the registration of their units. Instead what we have is a Google Form, which is free to create, and cannot be updated. This begs the question, what was the $400,000 budget for the rental registry spent on, and what are the fees charged to landlords and tenants being used for? Given these facts and unanswered questions, it is not reasonable to build further punitive policies based on incomplete data from a program that was not implemented as promised.

The renters of Salinas cannot afford to be the subjects of policy experiments, especially when past policy experiments of this nature have proven harmful to renters. We cite the following, peer reviewed studies as evidence (attached for the Council’s convenience and for the public record).

Research by Konstantin A. Kholodilin & Sebastian Kohl, published in the International Journal of Housing Policy (Feb., 2023), studies data from numerous regions and found the following:

The (re-)introduction of tenancy regulation in the form of rent controls, tenant protection or supply rationing is back on the agenda of policymakers in light of rent inflation in many global cities. While rent controls promise short-term relief, economists point to their negative long-run effects on new construction. This study presents new long-run data on both rent regulation and housing construction for 16 developed countries (1910–2016) and finds that more restrictive rental market legislation generally has a negative impact on both new housing construction and residential investment.

Furthermore, research by Rebecca Diamond, Tim McQuade, and Franklin Qian, published in the American Economic Review (Sep., 2019) examines the effects of rent control in San Francisco. Abstract:

Using a 1994 law change, we exploit quasi-experimental variation in the assignment of rent control in San Francisco to study its impacts on tenants and landlords. Leveraging new data tracking individuals’ migration, we find rent control limits renters’ mobility by 20 percent and lowers displacement from San Francisco. Landlords treated by rent control reduce rental housing supplies by 15 percent by selling to owner-occupants and redeveloping buildings. Thus, while rent control prevents displacement of incumbent renters in the short run, the lost rental housing supply likely drove up market rents in the long run, ultimately undermining the goals of the law.

We request that the Council take the time to consider this evidence, gather expert testimony through the TAC, and seek public feedback through meetings accessible to all community members. This will ensure policies are created with due diligence and as much relevant data as possible. Furthermore this will provide time for the rental registry to collect a more complete dataset, and for everyone to better understand how new State laws will affect rental housing.

Sincerely,
Adam Pinterits
Government & Community Affairs Director
Monterey County Association of Realtors®
Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=reuj20 International Journal of Housing Policy
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/reuj20 Do rent controls and other tenancy regulations
affect new construction? Some answers from long-
run historical evidence
Konstantin A. Kholodilin & Sebastian Kohl
To cite this article: Konstantin A. Kholodilin & Sebastian Kohl (2023): Do rent controls and other tenancy regulations affect new construction? Some answers from long-run historical evidence, International Journal of Housing Policy, DOI: 10.1080/19491247.2022.2164398 To link to this article: https://doi.org/10.1080/19491247.2022.2164398 © 2023 The Author(s). Published by Informa
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Internat Ional Journal of Hous Ing Pol Icy
Do rent controls and other tenancy regulations
affect new construction? Some answers from
long-run historical evidence
Konstantin A. Kholodilin a,b and Sebastian Kohl c,d
aMacroeconomics, DIW Berlin, Berlin, germany ; beconomics, nru H se, st. P etersburg, russia; cMax-Planck-Institut für gesellschaftsf orschung, cologne , germany ; dsociology, freie univ ersität Berlin, Berlin, germany

ABSTRACT
The (re-)introduction of tenancy regulation in the form of rent controls, tenant protection or supply rationing is back on the agenda of policymakers in light of rent inflation in many global cities. While rent controls promise short-term relief, economists point to their negative long-run effects on new construction.

This study presents new long-run data on both rent regulation and housing construction for 16 developed countries (1910–2016) and finds that more restrictive rental market legislation generally has a negative impact on both new housing construction and residential investment. This is especially true for strict rent controls and housing rationing measures in the post-1960 period.

Tenancy security can on average also dampen construction activity. The neg - ative effect is overall less significant and strong in magnitude than expected and may have been offset by exemptions for new construction, by compen - sating social housing construction and by a flight of new construction into the owner-occupied sector. Still, on average, rent controls came at the cost of less construction activity.

KEYWORDS: Residential construction; rent control; tenure security; housing rationing; panel data model
JEL CODES: C23; O18; R38
Introduction
Long thought to be a relic of the past, rent controls and other measures protecting urban tenants are back on the political agenda in a wide range of countries. Even if the move towards homeownership has made owner-occupying households the majority almost worldwide, many of larger cities possess significant tenant populations. They still make up an https://doi.org/10.1080/19491247.2022.2164398
© 2023 the author(s). Published b y Informa uK limit ed, trading as taylor & francis gr oup CONTACT sebastian Kohl [email protected]
this is an open acc ess article distributed under the terms of the crea tive commons attribution lic ense ( http:// creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2 K. A. KHOLODILIN AND S. KOHL
important constituency which advocates for rent regulation as a form of social policy.

Particularly in 2019, several countries and states have introduced new or reinforced measures to cap rent price increases. For example, in February 2019, the authorities of the state of Oregon (USA) imposed upper bounds on rent increases on the consumer price index (CPI) growth rate above 7%.
1 In June 2019, in New York, several measures to
enhance rental regulations were introduced, including the removal of the vacancy decontrol, which was previously possible, if rent prices for a dwelling or the income of tenant renting exceeded certain thresholds. 2 In February 2020, a so-called Mietendeckel (literally meaning ‘rental lid’) was introduced in Berlin (Germany) leading to a rent freeze for the fol - lowing five years and even providing the possibility to cut rents in the case they were found to be too high.
3 However, on 25 March 2021, the
rent freeze was abolished by the German constitutional court. During the September 2021 Bundestag elections, the issue of extending the rent freeze to the whole of Germany was debated. Similarly, a law was enacted in Catalonia (Spain) in September 2020 capping initial rents at a local reference rent (plus 10%) for non-luxury units in tense housing market areas. Simultaneously rent increases were pegged to the index of competitiveness recovery of Spain with respect to the European Monetary Union.
4 The Covid-19 pandemic provided an additional impetus for rent regulations. Thus, almost 50 countries introduced eviction bans, while over 20 countries enacted rent freezes, including rent reductions and moratoria of rents during the emergency period.
5 Yet, despite this surge
of regulations in private rental markets, housing scholars still focus pre - dominantly on social housing or homeownership as dominant segments in the housing market.

The renaissance of rent control even in the rigid forms of freezing rents introduced as first-generation controls during both World Wars is surprising, given the almost unanimous agreement among economists on the neg - ative effects of tenancy regulation in general and rent controls in particular on the allocation and supply of housing, as several surveys conducted among economists between 1979 and 2009 show (Kearl et al., 1979; Alston et al., 1992; Jenkins, 2009). Among the negative effects attributed to these market regulations the allegedly negative effect on new construction is probably the most prominent one. Richard Arnott also observed the ‘wide - spread agreement that rent control discourages new production’ (Arnott, 1995, p. 99). Restrictive housing market regulation such as protections from rent increases or evictions are thus made responsible for lowering construction activities and increasing housing shortages. They are seen as measures which reduce the incentives for investing in new residential construction, especially of rental housing, since governmental restrictions limit rental revenues and the freedom to dispose freely of one’s real estate property. Today’s climate of urban housing shortages in most booming European cities has led many economists to regard the removal of rent INTERNATIONAL JOURNAL OF HOUSING POLIC Y 3
regulations as stimulus for new housing supply (Diamond, 2018), even though the existing literature shows relatively mixed results.

This study investigates the relationship between restrictive governmental housing regulations—not just rent controls but also protection from evic - tion and housing rationing measures—and residential construction for the first time with international historical long-run data. It draws on two novel data collections ranging between 1910 and 2016 for 16 developed coun - tries: the first one contains rent regulation indices based on manual cod - ings of all major tenancy-related laws in a country, and the second one contains data on building activity. The regulation data include measures for three types of restrictive housing policies: rent control, security of tenancy and rationing of housing units. Our dependent variables are annual time series of new residential construction (housing units, invest - ment) and we control for economic (real GDP per capita, long-term interest rates and mortgage debt) and demographic factors (population growth, total dependency ratio and marriage rates) in panel-data models.

While our findings are broadly in line with economists’ general expec - tations, i.e., in normal and post-war periods, rent controls, tenancy-security and rationing regulation have on average negative effects on new con - struction activity, the surprising finding is that the significance is not as persistent as economists would expect. Whereas the severe rationing mea - sures are significant almost throughout, security of tenancy and rent price regulation are only significant under certain conditions: in their strict form of rent freezes and in the post-1960 period. Increasing the regulation by 1 on a scale from 0 to 1, i.e., shifting from zero to full control, decreases new construction by 0.06 per 1000 inhabitants, which is a sizeable mag - nitude when accumulated over several years, but also not a complete construction stopper.

We suggest that this rather surprising non-universality of a strong neg - ative tenancy-regulation effect could be explained by the fact that new construction has often been exempted from rent control. Moreover, tenancy regulation may crowd out rental units in favour of owner-occupied ones which can enjoy ongoing construction despite rent control (Kholodilin & Kohl, 2021b; Fetter, 2016). While this article only focuses on new construc - tion, the regulation effects on existing stock should also be kept in mind.

Finally, strict rent control rarely comes without active social housing policies in favour of new construction which can compensate for the loss of private construction. The findings also highlight that tenancy regulation beyond rent controls, and the war-related housing rationing measures, in particular, can significantly impact new construction and that for rent control effects themselves, the historical and country context matters. In that, our findings on rent control appear to be similar to mixed findings on the effect of minimum wages on labour supply, i.e., the labour market equivalent of price controls (Doucouliagos & Stanley, 2009).

Existing research on private rental markets and rent control is rather scarce, uses mostly geographically limited data for snapshot moments or 4 K. A. KHOLODILIN AND S. KOHL
the short run. Our main contribution is to expand on this understudied topic by extending the geographic and temporal scope of the analysis with the help of new international long-run data, which cover the historical time periods in which rent control was first introduced and most wide - spread. Against the backdrop of these long-run data, our findings confirm a long-held expectation about tenancy policies’ negative effects on build - ing activity, but also show that the effects are less consistent and smaller than expected. The implication of our findings is that strong rent controls or rationing measures, if not compensated by social housing construction, may have negative effects on housing construction and investment.

The study proceeds as follows. The next section reviews the literature on the effects of governmental regulations on residential construction.

The following section describes the data used and explains the method - ology applied in this study, while the third section discusses the results obtained. The last section points to potential interpretations of the main finding and concludes.

Determinants of residential construction
Rental housing market regulations and most prominently rent controls are a phenomenon that has attracted quite some attention from econ - omists. We identified 99 empirical studies published in peer-reviewed journals between 1967 and 2022 that overall cover 36 different coun - tries, with one-third of the studies being devoted to North America and more than a half of studies concentrating on European countries, the remainder focussing on the Global South (Kholodilin, 2022 ).

Together they investigate 19 different effects that rent controls can typically have, while many studies examine several impacts at a time.

Most authors are interested in the effects of rent control on rents of controlled apartments (31 studies), residential mobility (19), new hous - ing construction (12) and homeownership (11). Other studies are inter - ested in effects on welfare, segregation, misallocation, vacancies, quality, homelessness, etc.

This literature generally finds that rent regulation in the form of price controls significantly lowers controlled rents (and returns) but increases uncontrolled rents (e.g., Attia, 2016; Baye & Dinger, 2022 ; Ahrens et al.,
2019 ). At the same time, it tends to increase homeownership, as it crowds out rental housing units (Diamond et al., 2019; Asquith, 2019; Appelbaum et al., 1991; Fetter, 2016). Studies unanimously find that rent controls lower residential mobility, as tenants have a strong incentive to remain in controlled units (Gyourko & Linneman, 1989; Clark & Heskin, 1982
; Bonneval et al., 2022; Karpestam, 2022; Gardner, 2022). The liter - ature also agrees on the negative effects on housing quality, as landlords lose the means and incentive for proper maintenance (Gilderbloom & Ye, 2007
; Breidenbach et al., 2022; Tan, 2021).

INTERNATIONAL JOURNAL OF HOUSING POLIC Y 5
The number of studies investigating rent control effects on residential construction is already more limited and much more mixed in compar - ison to the articles studying other outcomes. We identified a total of 12 published studies. These studies mostly cover Canada, Scotland, Sweden and the United States. The estimation techniques are rather rudimentary, except for Sims ( 2007) who uses difference-in-differences regressions, although the authors are sometimes very inventive regarding their data sources. Most studies (seven out of 12) find a negative impact of rent control (Lind, 2003; Smith & Tomlinson, 1981; Smith, 1988) or a positive effect of deregulation (Bailey, 1999; Gibb, 1994) on new housing construction. Only Gilderbloom and Ye ( 2007
) and Ambrosius et al.
(2015), using more or less the same data and the same methodology as Gilderbloom and Markham ( 1996), find no impact of rent control on new residential construction. Moreover, in this particular case, rent con - trols are thought to be moderate. Goetz (1995) concludes that the multifamily-housing production in San Francisco has accelerated after the introduction of rent control. However, he does not control for other factors, except for dummy variables of rent control introduction, that could explain higher construction rates after rent control was adopted in 1979.

The best evidence comes from sub-national case studies. The most prominent recent quasi-experimental study of San Francisco estimated the effect of rent controls on new construction as high as a 15% reduction in new supply (Diamond et al., 2019). Also studying San Francisco, Asquith (2019) finds a reduction in rental housing supply, as landlords sell off apartments in the condo-market or simply hold back supply. A similar phenomenon—a conversion of rental into owner-occupied units—was found by Fetter ( 2016) for the US rent controls during and immediately after World War II (WWII). However, Sims ( 2007), using microdata from a housing survey conducted in Massachusetts in 1985–1998, finds little effect of rent control on new housing construction. Studying the same de-control moment, Autor et al. ( 2014) also find a very low effect of de-control on new residential investment. Mense et al. ( 2018), who investigate a recent strengthening of German rental policy—the rental brake ( Mietpreisbremse)— establish that it fostered new construction in the controlled municipalities.

Overall, the existing literature on the construction and supply effects has predominantly focussed on the effects of rent price controls and not on other forms of tenancy regulation (such as supply restrictions or tenant protection). The existing macro-studies have been rather narrow in terms of geographic scope and regarding the length of time series data used.

Almost all studies ignore the historical moments with highest rent regu - lation activity. We address these shortcomings below by increasing both geographic and time coverage and by including different dimensions of tenancy regulation.
6 K. A. KHOLODILIN AND S. KOHL
Data
In this section, we present the variables and operationalisation used in this study. For the sake of convenience, we split them into tenancy reg - ulation indices, dependent construction variables and control variables.

Table 1 reports the sources of data in more detail and presents the descrip - tive statistics.

Regulation indices
The main explanatory variable is the intensity of tenancy regulation which we approximate by drawing on the restrictive rental market regulations indices, as developed by Kholodilin ( 2020) and Weber ( 2017). They cover three types of regulations: rent control, tenure security and housing rationing. The rent control index measures the intensity of restrictions imposed on the level of rent and its rate of increase. This index is com - puted as a simple average of six binary indices reflecting the following policies: real rent freeze, nominal rent freeze, rent level control, inter-ten - ancy decontrol, other specific rent decontrol and specific rent re-control.

Thus, the rent control index varies on a continuous scale between 0 and 1.

Moreover, economists distinguish between the first and the second generation of rent control (Arnott, 1995). The first generation implies a hard rent freeze, when rents are fixed at a given level, while under the softer second-generation rent control, the starting rent is generally set at market level, but its growth rate is tied to a measure of living or building costs. Here, we use regulation indices of the first- and second- genera tion rent control. In case of the first-generation rent control, there are both real and nominal rent freezes as well as rent level controls. By contrast, under the second-generation rent control, only real rent freezes are pres - ent. The Rent laws index, on the one hand, and first- and second-gener - ation rent control indices, on the other hand, are constructed in a different way. Unlike the continuous Rent laws index, the generation indices of rent control are binary indices—being equal either to 0 or to 1—reflecting whether the state uses the first or second generation of rent control or not. Therefore, they contain related but not the same information.

The tenure security index, in turn, reflects the degree of protection of tenants from evictions by landlords. The main instruments of protection are eviction protection during a given lease term or period; eviction pro - tection at the end of the term or period; imposition of a minimum duration of rental contracts; and a prohibition of short-term tenancies (of less than one year).

Finally, the housing rationing index measures the intensity of redistri - bution within the existing housing stock. It includes such policies as registration of housing; protection of housing (e.g., prohibitions to convert residential premises to other uses or to short-term rentals); requisition of vacant housing; restriction of freedom to move into areas with tight INTERNATIONAL JOURNAL OF HOUSING POLIC Y 7
Table 1. Description of variables used in the analysis.

Variable description source Period Minimum Mean Maximum standard deviation Housing completions by 1000 inhabitants Kohl (2021) 1900–2016 0.020 5.447 17.314 2.928 share of residential construction in gDP , % Macrohistory 1900–2017 0.039 4.806 12.081 2.079 rent laws index, (0, 1) rHMr 1900–2021 0 0.488 1 0.361
first-generation rent control index, (0, 1) rHMr 1900–2021 0 0.493 1 0.500 second-generation rent control index, (0, 1) rHMr 1900–2021 0 0.128 1 0.334 tenure security index, (0, 1) rHMr 1900–2021 0 0.393 1 0.245 Housing rationing index, (0, 1) rHMr 1900–2021 0 0.109 0.750 0.165 rental market regulation index, (0, 1) rHMr 1900–2021 0 0.440 0.917 0.255 real gDP per capita, 1990 in ternational geary –Khamis
dollars, 1000
Maddison 1900–2018 1.833 17.116 84.580 14.156
real per-capita gDP g rowth rate own calculations 1901–2018 −0.878 0.020 0.506 0.056 long-term interest rate, % Macrohistory and oecD 1900–2018 −0.251 5.675 21.502 3.180 total loans-to- gDP r atio, % Macrohistory and own
calculations
1900–2016 0.019 0.641 2.045 0.365
Population growth Maddison and own
calculations
1901–2018 −0.075 0.007 0.034 0.006
Housing return minus equity return (relative rate of
return)
Macrohistory 1900–2015 −0.096 0.014 0.102 0.025
government budget balance-to- gDP r atio Macrohistory and own calculations
1900–2016 −0.752 −0.022 0.201 0.059
ratio of dependent (younger than 15 and older than
64 y. o.) population to working-age (15 through
64 y. o.) population, [0,1]
World Development
Indicators of the World
Bank and european
university Institute
1900–2016 0.425 0.544 0.993 0.067
number of marriages per 1000 population Mitchell ( 2013) and oecD Vital statistics
1900–2016 2.113 7.058 17.959 1.838
Note: BI s = Bank for International settlements ( https://www.bis.org/statistics/pp_detailed.htm ); federal reserv e Bank of Dallas ( https://www.dallasfed.org/institute/house - price#tab2); Macrohistory = Jordà–schularick–taylor M acrohistory Database ( http://www.macrohistory.net/data/ ); Maddison = Maddison H istorical statistics ( https://www. rug.nl/ggdc/historicaldevelopment/maddison/ ); oecD = o rganisation for economic coopera tion and Development Housing prices data ( https://data.oecd.org/price/hous - ing-prices.htm ); rHMr = r ental Housing M arket regulation da tabase ( https://rpubs.com/Konstantin_Xo/ rHMr). 8 K. A. KHOLODILIN AND S. KOHL
housing markets; conservation of social composition of the neighbour - hoods in order to prevent gentrification; imposition of maximum housing consumption norms; and nationalisation of private housing stock.

All three indices range between 0 and 1: the higher the index the more intense the regulation. In addition, following Weber ( 2017), we computed a rental market regulation index ( RMRI) as a simple average of the rent control index and tenure security index. Thus, it measures an overall secu - rity of tenants by simultaneously capturing the protection of tenants from rent increases and from eviction.

The indices are constructed based on a manual content coding of the corresponding legal acts (see https://rpubs.com/Konstantin_Xo/RHMR for details) and, to the best of our knowledge, represent the best available data source for comparative long-run measures of tenancy regulation. It is important to note that the indices do not measure how tight each regulation in place is, but whether regulations of different sorts are gen - erally in place or not and how many of them are enacted. It is also important to keep in mind that this law-based approach cannot deal with different degrees of enforcement of laws. A final note of caution is that these laws are enacted on the national level and do not account for regional variations. In most cases, the national focus reflects the most important regulation level, with decentralisation of housing policies starting in the 1980s in many countries. The indices thus measure, for instance, that the US federal government does not implement any rent controls and that this differs from Germany, which allows municipalities to use regionally specific comparative-rent tables to enforce soft rent controls, but the index ignores differences between the enforcement in Berlin and Cologne.

While these are some obvious shortcomings of the indices, they are the only available long-run regulation data at hand. There are two addi - tional arguments speaking in their favour. First, our indices correlate quite well with alternative ones, as shown in Kholodilin ( 2020). Second, there are already quite a few researchers, including those from the IMF and the OECD, who are using the indices for their research, where they have become a common data currency.
6 Housing construction intensity
Our dependent variable is housing construction intensity, which is defined as the number of completed dwellings per 1000 persons (cf.

Kohl 2021). Surveying construction requires a certain governmental con - trol of property rights and of the construction sector which is not given in many developing nations and therefore restricts the countries we can sensibly include in the study. Construction volume is available as permits, starts and completions and with the exception of a few countries such as the US, completions are reported throughout. The advantage of INTERNATIONAL JOURNAL OF HOUSING POLIC Y 9
housing starts (and permits) as measure is that they are the most sen - sitive measures to reveal macroeconomic impacts on initiated construc - tion activity. Their obvious shortcoming is that not all housing starts end up in completions due to construction-loan problems, bad calcula - tions or speculation. Completions, in turn, have the disadvantage that they lag behind starts by one or two years. However, they indicate what has been constructed and their coverage across countries is highest. For these reasons, we choose completions as our measure for new construc - tion volume.

To control for demography right from the start, we divide completions by the current population, which yields a commonly used variable in the range of 2 to 15 completed units per 1000 inhabitants (cf. Kohl 2021). In cases of missing completion data due to countries not having surveyed them at all or only at certain points in time, we approximate completions through housing starts and permits. Our rule of approximation is the following: If available, we use the first lag of housing starts multiplied by the median ratio of housing completions and starts in our sample exclud - ing the war and post-war years, namely 0.98. If starts are also not available, we use the first lag of permits again multiplied by the average ratio of housing completions and permits, namely 0.95. This is to make sure that the levels of completions are approximated, as the over-time trends are highly similar. For the available data, both lagged permits and lagged housing starts strongly correlate ( r
= 0.98).

To include new housing quality and investments in existing stock, we also rely on total residential construction investment per GDP as an alter - native dependent variable, which comes with the advantage of being a monetary variable, but is therefore also subject to price effects.

Control variables
The existing literature usually points to a list of control variables, mainly the common economic and demographic background variables, which need to be available for the very long-run for our purposes. On the eco - nomic side, we control for GDP per capita as higher income levels allow for more construction to take place. The business cycle is also known for its strong correlation with the building cycle (Leamer, 2007). With govern - ment activity being important for the building sector, we also include the governmental budget balance as a variable. Most new construction is not financed out of equity, which is why capital markets play a crucial role.

We, therefore, include long-term interest rates that govern mortgage lend - ing. Moreover, we include the growth of mortgages outstanding to GDP:
in normal times, more mortgage supply should lead to new construction, but we also include its quadratic term, as too high levels of mortgage indebtedness has been found to just drive up prices and to not extend supply further (Kohl, 2021). New construction depends on the relative 10 K. A. KHOLODILIN AND S. KOHL
attractiveness to build; we, therefore, include the relative rate of return computed as a difference between housing rental returns and stock market returns in the estimation. Finally, we would include homeownership rates, but their coverage reduces our sample to a post-1950 sample, giving away our long-run data advantage.
7 On the demographic side, we control for population growth to account for rising demand. As a more refined measure, we also control for marriage rates per population because they indicate the formation of new house - holds. Family formation requires the extension of living space, whereas older household cut back living space at higher ages. We therefore also include a dependency ratio by interpolating the age composition of the population surveyed at census points.
8 Econometric methodology
Methodologically, the availability of longitudinal data suggests the use of a panel data model. Given the strong persistence of construction intensity and in order to remove serial correlation and potential non-stationarity, we compute the dependent variable as the first difference of the log of construction intensity.
yx zvit it it it it/g32/g14 /g14/g14/g14/g99
/g16
/g99
/g16/g69/g74 /g75/g84,, 12 (1)
where yit is the first difference of the construction intensity or the per - centage of residential construction in GDP in country i in the year t; xit is the vector of control variables; zit is the vector of regulation indices; ηi is the country fixed effects; θt is the time fixed effects; vit is the random disturbance; and β is the vector of coefficients.

We transform the explanatory variables that Dickey-Fuller tests reveal to be non-stationary (population, mortgages and GDP per capita) into growth rates or first differences which also transforms some of these stock-variables into flows, more apt to explain the flow of new construc - tions. We use the second lag of the regulation indices in order to capture the fact that housing construction takes time to reach completion. The plot of land must be found, the architectural plans must be made, the building permit must be obtained and finally, the house must be built.

All these procedures take time and on average two years can pass between the decision to build and the completion. In addition, factors such as bad weather and unavailability of subcontractors and workers during periods of busy construction activity can lengthen the process even more. For example, according to the US Census Bureau 2020 sta - tistics, it takes on average about 7 to 16 months bet ween the start and completion of single- and multi-family houses, respectively. 9 For the control variables we use their first lags. Given that we work with annual data, one lag should be sufficient. Wooldridge ( 2012, p. 658), for instance, INTERNATIONAL JOURNAL OF HOUSING POLIC Y 11
suggests to use at most two lags for annual time series. A larger number of lags would reduce the already modest degrees of freedom in our models.

Results
We first describe how construction and tenancy regulation developed across time and different regions to then present the multivariate results.

Descriptive findings
Figure 1 depicts the evolution of the three regulation indices between 1900 and 2021. All curves show a two-hump structure: regulation set in with WWI as consumer socialism for the home front of soldiers’ families.

Figure 1. r esidential construction intensity and rental housing market regulations.

Note: construction intensity is measured as the number of completed dwellings per 1000 persons. the indices of r ent control, tenure security and housing rationing vary between 0 (no rent control) and 1 (very strict control). all indices ar e obtained by averaging from the country-specific indicators of 16 countries under investigation. 12 K. A. KHOLODILIN AND S. KOHL
It was then reversed during the interwar years only to spike again during WWII. But whereas the rationing supply side restrictions were almost com - pletely dismantled in the post-war development, tenancy security was on average maintained throughout all jurisdictions. Rent regulation itself reduced in intensity everywhere: the strong first-generation freezes of abso - lute rent levels softened into second-generation regulation of rent increases.

While this is the broad common story across jurisdictions, there are notable differences between low-regulation Anglophone countries and continental European countries with a tradition of stronger tenancy regulation.

In addition, Figure 1 shows the long-run construction cycle. With the exception of socialist countries, there is a rough counter-cyclical movement of construction and rent regulation over the 20
th century: rent regulation
surges in war times when construction is low and it fades out with the building cycle taking off. Towards the end of the reporting period, the negative correlation is less evident as building cycles can occur even at a constant rent level.

Multivariate estimations
In what follows, we estimate four different models, depending on the combination of rental regulation indices and control variables included.

We choose these models to uncover different combinations of the regu - lation indices (models 1–2), to distinguish soft from hard rent controls (3) and to uncover potential non-linear effects (4). We estimate these models for two dependent variables: completed housing units per population and residential investments per GDP .
10 We include a total of 16 countries: with
the exception of Portugal, all countries start at least in the interwar period, most even before the First World War.

Tables 2 and 3 report estimation results for housing completions for the whole sample and the war-unaffected, post-1960 subsample, respec - tively. We distinguish war- from post-war-times because the former are arguably very unrepresentative times for housing policies and rent policies in particular. A first observation is that the different combinations of reg - ulation indices share a persistent negative coefficient sign (with the excep - tion of second generation controls), but at low significance levels. A closer look shows that three regulation indices are statistically significant: tenancy security in the whole sample, housing rationing and rent price controls in the war-unaffected subsample. The effect of the general rent price regulation is entirely driven by the remaining hard first-generation price controls. All affect the intensity of residential construction negatively. Thus, stricter regulations, limiting the freedom of landlords to set prices and to select tenants, diminish the incentives to build new housing.

Table 2 presents estimation results for the whole sample using the intensity of housing construction as a dependent variable.

Regarding the magnitude of the effect, it is sizeable, but also not extremely large. The linear effects are the smallest for the models INTERNATIONAL JOURNAL OF HOUSING POLIC Y 13
estimated over the whole period: the largest decline of the growth rate of construction intensity is obtained for the maximum rent control intensity (equal to 1) and is between −1.3% for all and −1.6% for devel - oped economies. For the post-1960 period, the effects are much larger, varying from the largest decline of about −6% for all countries to almost −8% for developed ones. Assume that in the initial situation (period t) there is no rent control and that construction intensity is 10 dwellings per 1000 inhabitants. If in the following year ( t
+ 1) the stric test possible
rent control is introduced, in t + 3 the construction intensity would fall to 9.84–9.87 dwellings per 1000 persons for the whole period and to

9.2–9.4 dwellings per 1000 persons for the post-1960 period. In a coun - try with 100 million inhabitants, it would correspond to a reduction in residential construction by 13,000–16,000 and 60,000–80,000 dwellings, respectively, which is a sizeable magnitude over several years, but also not a complete construction stop.

Table 2. e stimation results of panel data model: construction intensity, whole period.

Dependent variable: growth rate of construction intensity Model 1 Model 2 Model 3 Model 4
rent laws t−2 −0.026 −0.160
(0.036) (0.111)
rent laws t−2
2 0.127
(0.110)
first-generation rent control t−2 −0.031
(0.020)
second-generation rent control t−2 0.013
(0.027)
tenure security t−2 −0.083* −0.102* −0.073
(0.041) (0.051) (0.039)
rationingt−2 −0.081 −0.082 −0.071 −0.087
(0.095) (0.096) (0.096) (0.099)
rMrIt−2 −0.094
(0.063)
Per-capita gDP growth t−1 0.252 0.256 0.260 0.256
(0.179) (0.179) (0.181) (0.177)
long-term interest rate t−1 −0.003* −0.003* −0.002 −0.003 (0.001) (0.001) (0.001) (0.002)
relative rate of return t−1 −0.045 0.007 0.014 −0.004
(0.222) (0.225) (0.232) (0.227)
change in loan-to- gDP r atiot−1 0.177 0.189 0.185 0.195 (0.176) (0.179) (0.177) (0.180)
change in loan-to- gDP r atio
t−1
2 −5.032*** −5.142*** −5.068*** −5.140***
(1.079) (1.029) (1.056) (1.088)
government balance-to- gDP r atiot−1 0.015 −0.013 0.030 −0.001 (0.191) (0.179) (0.190) (0.197)
Population growth t−1 1.874 1.593 1.816 1.792
(1.637) (1.634) (1.701) (1.667)
Dependency ratio t−1 −0.275 −0.292 −0.314 −0.279
(0.196) (0.212) (0.203) (0.195)
Marriage rate t−1 0.027* 0.028* 0.028* 0.027*
(0.011) (0.011) (0.011) (0.011)
r2 0.054 0.052 0.057 0.055
number of observations 1005 1005 1005 1005
***p<0.001, ** p<0.01, * p<0.05.
14 K. A. KHOLODILIN AND S. KOHL
Over the whole sample, growth of real per-capita GDP is statistically significant among the control variables. It exerts a strong positive effect on the construction intensity, which corroborates economic theory and common sense. Higher interest rates, in turn, rather depress new con - struction, whereas more mortgage debt (insignificantly) increases con - struction, but only up to a certain point (significantly negative square term). Among the demographic variables, marriage rates have a statistically significant positive effect. Even when people marry less, marriage rates might be a proxy for years when demographic cycles produce more family formation and hence construction demand. The addition of demographic and particularly economic variables normally associated with the building cycle takes away the significance of regulation indices.

Much of the regulation effect is driven by the war-time effects as the war-unaffected subsample results in Table 3 shows: post-1960, rent and Table 3. e stimation results of panel data model: construction intensity, 1960–2016.

Dependent variable: growth rate of construction intensity Model 1 Model 2 Model 3 Model 4
rent laws t−2 −0.085** −0.013
(0.027) (0.104)
rent laws t−1
2 −0.069
(0.100)
first-generation rent control t−2 −0.043*
(0.019)
second-generation rent control t−2 0.003
(0.025)
tenure security t−2 −0.069 −0.087 −0.072
(0.036) (0.046) (0.038)
rationingt−2 −0.221*** −0.221*** −0.231*** −0.224***
(0.056) (0.054) (0.061) (0.055)
rMrIt−2 −0.157**
(0.050)
Per-capita gDP growth t−1 0.629** 0.627** 0.629** 0.628** (0.207) (0.202) (0.206) (0.207)
long-term interest rate t−1 −0.003* −0.003* −0.002 −0.003* (0.002) (0.002) (0.001) (0.002)
relative rate of return t−1 0.162 0.142 0.169 0.160
(0.324) (0.305) (0.341) (0.325)
change in loan-to- gDP r atiot−1 0.153 0.150 0.140 0.140 (0.196) (0.196) (0.198) (0.196)
change in loan-to- gDP r atio t−1
2 −3.144* −3.113* −3.153* −3.090*
(1.276) (1.298) (1.276) (1.286)
government balance-to- gDP r atiot−1 0.303 0.310 0.337 0.311 (0.201) (0.193) (0.212) (0.202)
Population growth t−1 1.443 1.499 1.405 1.639
(0.774) (0.798) (0.897) (0.873)
Dependency ratio t−1 −0.055 −0.054 −0.030 −0.070
(0.129) (0.127) (0.141) (0.141)
Marriage rate t−1 0.026*** 0.026*** 0.027*** 0.026***
(0.007) (0.007) (0.007) (0.007)
r2 0.095 0.095 0.094 0.096
number of observations 807 807 807 807
***p<0.001, ** p<0.01, * p<0.05.

INTERNATIONAL JOURNAL OF HOUSING POLIC Y 15
rationing regulation indices are negatively associated with construction intensity and significantly so. Apparently, the housing rationing, being one of the most drastic measures, has a strong negative impact on the confidence of investors and, therefore, on their willingness to build new houses. Thus, the threat of being expropriated represents an effective obstacle to residential construction. The rent control effect is again driven by the strict rent freezes and not the second-generation controls.

The coefficient estimates obtained for the alternative dependent vari - able, construction investment per GDP , are very consistent with those obtained for the construction intensity (not shown). Restrictive housing policies appear to exert a negative impact on residential construction, especially since 1960, but at low significance levels. For the more recent period, rent price controls have again a significantly negative effect, mostly driven again by the first-generation rent controls. Supply rationing mea - sures are highly significant in this period and reduce new constructions.

Tenure security regulations are only statistically significant for the estima - tion over the whole sample. Increasing tenancy regulation to a maximum leads to a decrease of residential investment per GDP of 0.085 percentage points and of up to 0.043 for the strict first-generation controls. The control variables behave very similarly to the results in the previous two estimates.

Although we find negative effects of restrictive regulations on housing construction, these effects are often not statistically significant. It is possible that due to factors internal to each country positive and neg - ative effects in different types of countries cancel each other out. This issue could theoretically be addressed by focussing on specific countries.

However, such a country-specific analysis is rather difficult due to the limited number of observations per country. This is the reason why we opted for using the panel-data approach that dramatically increases the number of observations. To some extent, the country-specific effects are captured by the fixed effects. However, it can be true that an offsetting of effects in various countries occurs. Nevertheless, as an overview of the empirical literature on rent control effects shows, most studies using different methodologies and data find that rent control exerts either a negative or no impact on housing construction (Kholodilin, 2022). Thus, the cancelling out of effects could hardly affect our main conclusion.

Discussion and conclusion
The general finding points to the expected negative effect of rent regu - lation on new residential construction: throughout different samples and specifications, the regulation coefficients are negative, albeit with changing levels of significance. The finding is most persistent for rationing and first-generation rent controls and most pronounced for the war-unaffected period which reaches up to the current day. Higher levels of security of tenure can depress new construction. However, this finding is not always confirmed, as the corresponding coefficient is only statistically significant 16 K. A. KHOLODILIN AND S. KOHL
in the total sample. Overall, with the most complete historical-comparative data available, the received wisdom among economists of a negative construction and investment effect of rent controls seems to hold, but with far less persistence and magnitude than is usually assumed. In many models, new construction depends more on economic or demographic factors than on tenancy regulation. This is in line with the rather mixed results of existing studies of smaller geographic and temporal scope, where almost half of the studies produce null findings.

The models covering the whole time period do not find any significant rent-control effect other than for the war-related first-generation controls and for the rationing measures. One explanation could be that lower rent control intensity corresponds to the more flexible first-generation rent con - trol, while higher ones correspond to the much more restrictive first-gen - eration rent control. This is in line with previous research (Mense et al., 2018). Finally, one could argue that the magnitude of the examined effects is not particularly high in general. It is not the case that private construction completely stops after the introduction of rent control measures.

What could explain the deviance from the usually expected result? In the following, we want to discuss potential data and model configurations which could explain our main results. One obvious explanation is that rent control laws often exempt new construction from regulations. A sub- item in the regulation data set measures whether exceptions to rent controls exist (e.g., for the luxury market segment, new construction, and certain geographies). In the global sample, these exceptions were present in about 70% of all country years during which rent legislation was in place. Unfortunately, the variable is coded too broadly, not taking into account kind and degree, to produce any significant results. Yet, the pur - pose of many exceptions is to guarantee that the incentives for building new houses are not diminished. Thus, the rents for newly built dwellings are not controlled and so the investors can earn decent profits on it. But investors might still shy away from further investment in rental stock, as they might expect a general deterioration of investment climate and a slippery slope towards even more state intervention.

A second explanation can draw on the fact that tenancy regulation crowds out rental dwellings in favour of owner-occupied ones in the exist - ing housing stock (Fetter, 2016; Kholodilin & Kohl, 2021b). What holds for the existing housing stock may as well hold for new construction: A poten - tial reduction in completions of rental dwellings could thus be more than offset by the increase in the completion numbers in the owner-occupied housing segment. This is all the more probable, given the evidence that homeowner-dominated societies are more prone to speculative house price dynamics (Rünstler, 2016). Homeowners or would-be homeowners who observe house price increases and expect them to continue are eager to participate in the overall speculative movement hoping to obtain capital gains. Thus, more housing is built in such economies than in the tenant-dom - inated ones, where most people are rather unwilling to see house prices INTERNATIONAL JOURNAL OF HOUSING POLIC Y 17
increases, since this often goes hand in hand with rent increases. It should be noted also that the switch from tenant to homeowner dominance can be the result of too strict rental regulations. In principle, one would need to replace our current dependent variable of all new constructions by new rental constructions, but the future use of a housing unit is unfortunately not known, let alone surveyed at the point of its construction.

A final explanation for why rent regulation is not universally affecting new construction resides in the fact that in many historical cases the restrictive rental measures are accompanied by housing policies seeking to foster the building activities through social housing or the stimulation of more private housing construction. This has especially been the case after major housing supply shocks caused by wars or natural catastrophes.

The inevitable rent increases are anticipated by using rent controls and the resulting unwillingness to build by private investors makes the gov - ernment step in to either replace the private building initiative or stimulate it artificially. This could also explain why the global sample results show less significant results than the shorter sample estimates as they included the period of strongest state intervention in housing markets, including social housing construction.

What then are the implications? Rent control measures of even the hard first-generation rent freezes or rationing measures are currently debated and passed in European countries and beyond. Even though they are often introduced with good intentions as social policy in favour of tenants and even potentially lowering short-term inequalities (Kholodilin & Kohl, 2021a), our results suggest that economists do have a point when warning about unintended consequences of depressing new construction. Rent controls help sitting tenants in the short run but contribute to future housing shortages for new tenants in the longer run. This long-run result can partially be offset by additional state policies stimulating housing construction. However, under rent control, the efforts to spur residential construction have to be much larger than in its absence. This undermines the frequently used argument that rent control is an interim measure deployed in order to combat rent increases, while awaiting for construction to gain momentum. Therefore, if one wants to overcome housing shortages as soon as possible, it may be better to abstain from restricting rents, especially from using strict first-generation rent controls.

Notes

1. Senate Bill 608 relating to residential tenancies; creating new provisions; amending ORS 90.100,

90.220, 90.323, 90.427, 90.600, 90.643, 90.675 and 105.124; and declaring an emergency.

2. Housing Stability and Tenant Protection Act of 2019.

3. See ‘Gesetz zur Mietenbegrenzung im Wohnungswesen in Berlin (MietenWoG Bln)’ as of 11 February 2020. The law was enacted on 23 February 2020.

4. Ley 11/2020, de 18 de septiembre, de medidas urgentes en materia de contención de rentas en los contratos de arrendamiento de vivienda y de modificación de la Ley 18/2007, de la Ley 24/2015 y 18 K. A. KHOLODILIN AND S. KOHL
de la Ley 4/2016, relativas a la protección del derecho a la vivienda . This regulation was appar - ently influenced to a large extent by the so-called rental brake ( Mietpreisbremse ) introduced in Germany in 2015. However, in March 2022, the Catalan rent control law was revoked by the Spanish Constitutional Court as unconstitutional— Sentencia de Tribunal Constitucional de España—Sentencia 37/2022, de 10 de marzo de 2022. Recurso de inconstitucionalidad 6289-2020 .

5. See https://rpubs.com/Konstantin_Xo/COVID19_housing_policies .

6. To name just a few: (1) Cournède et al. ( 2019) find that a tighter rental regulation tends to exacerbate the risk of severe economic downturns; (2) Cavalleri et al. ( 2019) suggest that restrictive rental market regulations can decrease the price elasticity of housing supply; while (3) Elfayoumi et al. ( 2021) investigate the link between rental market regulations and afford - ability of rental housing.

7. In such a regression, the homeownership variable does not have significant effects.

8. Marriage rates and age composition are interpolated using the R-package stinepack based on Stineman ( 1980).

9. Average Length of Time from Start to Completion of New Privately Owned Residential Buildings; https://www.census.gov/construction/nrc/pdf/avg_starttocomp.pdf . The larger the building, the lengthier the process: it takes 18 months f or buildings with 20 units and more. Thus, chang - es in regulations affect the willingness of investors to apply for permits. Only after the permits are obtained the construction can begin.

10. The results for the second dependent variable are available upon request, and more docu - mentation is shown in the longer working paper version.

Disclosure statement
The authors confirm that the article involves no conflicts of interest.

ORCID
Sebastian Kohl http://orcid.org/0000-0002-8358-6021
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American Economic Review 2019, 109(9): 3365–3394
https://doi.org/10.1257/aer.20181289
3365
The Effects of Rent Control Expansion on Tenants,
Landlords, and Inequality: Evidence from San Francisco† By Rebecca Diamond, Tim McQuade, and Franklin Qian*
Using a 1994 law change, we exploit quasi-experimental variation in the assignment of rent control in San Francisco to study its impacts on tenants and landlords. Leveraging new data tracking individuals’ migration, we find rent control limits renters’ mobility by 20 per
cent
and lowers displacement from San Francisco. Landlords treated by rent control reduce rental housing supplies by 15
per
cent by selling to
owner-occupants and redeveloping buildings. Thus, while rent con- trol prevents displacement of incumbent renters in the short run, the lost rental housing supply likely drove up market rents in the long run, ultimately undermining the goals of the law. (JEL R23, R31, R38) Steadily rising housing rents in many of the United State’s large, productive cities has brought the issue of affordable housing to the forefront of the policy debate and reignited the discussion over expanding or enacting rent control provisions. While the details of rent control regulations vary some across places, they generally regu- late rent increases and place restrictions on evictions. State lawmakers in California, Colorado, Illinois, and Oregon have considered repealing laws that limit cities’ abil- ities to pass or expand rent control. Rent control is already extremely popular around the San Francisco Bay Area. Nine Bay Area cities already impose rent control reg- ulations, two of which recently passed rent control laws through majority votes on the November 2016 ballot.

A substantial body of economic research has warned about potential negative efficiency consequences of limiting rent increases below market rates, includ- ing overconsumption of housing by tenants of rent-controlled apartments (Olsen 1972, Gyourko and Linneman 1989), misallocation of heterogeneous housing to heterogeneous tenants (Suen 1989, Glaeser and Luttmer 2003, Sims 2011, Bulow and Klemperer 2012), negative spillovers onto neighboring housing (Sims 2007; Autor, Palmer, and Pathak 2014) and neglect of required maintenance (Downs 1988).

Yet, due to incomplete markets, in the absence of rent control, many tenants are unable to insure themselves against rent increases. Of course, individuals who have little connection to any specific area may be able to easily insure themselves ag
ainst
* Diamond: Stanford Graduate School of Business, 655 Knight W ay, Stanford, CA 94305, and NBER (email:
[email protected]); McQuade: Stanford Graduate School of Business, 655 Knight Way, Stanford, CA 94305 (email: [email protected]); Qian: Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA 94305 (email: [email protected]). Thomas Lemieux was the coeditor for this article. We are grateful for comments from Ed Glaeser, Christopher Palmer, Paul Scott, and seminar and conference participants. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. † Go to https://doi.org/10.1257/aer.20181289 to visit the article page for additional materials and author disclosure statements.
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local rental price appreciation by simply moving to a cheaper location. However, if long-term tenants have developed neighborhood-specific capital, such as a net- work of friends and family, proximity to one’s job, or proximity to the schools of one’s children, then these tenants face large risks from rent appreciation. A variety of affordable housing advocates have argued that many tenants greatly value such insurance and that rent control can effectively provide it.

Despite the policy interest, due to a lack of detailed data and natural experiments, we have little well-identified empirical evidence evaluating how introducing local rent controls affects tenants, landlords, and the broader housing market. 1 In this
paper, we bring to bear new microdata and exploit quasi-experimental variation in the assignment of rent control to fill this gap. We exploit an unexpected 1994 law change that suddenly rent-controlled a subset of San Francisco buildings and their tenants, based on the year each building was built. However, the law left very similar buildings and tenants without rent control. We find tenants covered by rent control do place a substantial value on the benefit, as revealed by their choice to remain in their apartments longer than those without rent control. Indeed, we find the vast majority of those incentivized to remain in their rent-controlled apartment would have been displaced from San Francisco had they not been covered.

However, landlords of properties affected by the law change respond over the long term by substituting to other types of real estate, in particular by converting to condos and redeveloping buildings so as to exempt them from rent control. In the long run, landlords’ substitution toward owner-occupied and newly constructed rental housing not only lowered the supply of rental housing in the city, but also shifted the city’s housing supply toward less affordable types of housing that likely cater to the tastes of higher income individuals. Ultimately, these endogenous shifts in the housing supply likely drove up citywide rents, damaging housing affordability for future renters, and counteracting the stated claims of the law.

In 1979, San Francisco imposed rent control on all standing buildings with five or more apartments. While all large buildings built as of 1979 would now be rent-con- trolled, new construction was exempt from the law, since legislators did not want to discourage new development. In addition, smaller multi-family buildings were exempt from rent control since they were viewed as more “mom and pop” ventures, and did not have market power over rents. However, this small multi-family exemp- tion was lifted through a 1994 San Francisco ballot initiative. Proponents of this law change argued small multi-family housing was now primarily owned by large businesses and should face the same rent control restrictions of large multi-family housing. Since the initial 1979 rent control law only impacted properties built from 1979 and earlier, the removal of the small multi-family exemption also only affected properties built 1979 and earlier. This led to quasi-experimental rent control expan- sion in 1994 based on whether the small multi-family housing was built prior to or post 1980.

To examine rent control’s effects on tenant migration and neighborhood choices, we make use of new panel data which provide address-level migration decisions 1 Notable exceptions to this are Sims (2007) and Autor, Palmer, and Pathak (2014) which use the repeal of rent control in Cambridge, Massachusetts to study its spillover effects onto nearby property values and building mainte- nance. Neither one of these papers, however, directly studies how rent control impacts tenants. 3367
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 and housing characteristics for the majority of adults living in San Francisco in the early 1990s. This allows us to define our treatment group as renters who lived in small multi-family apartment buildings built prior to 1980 and our control group as renters living in small multi-family housing built between 1980 and 1990. Using our data, we can follow each of these groups over time up until the present, regardless of where they migrate.

We find that between five and ten years after the law change, the beneficiaries of rent control are, on average, 3.5 percentage points more likely to still remain at their 1994 address relative to the control group. Since only 18 percent of the con-
trol group still remained at their 1994 address for this long, this estimate represents a 19.4 percent increase in not mo
ving (3.5/18) relative to the control group. We
further find that the beneficiaries are 4.5 percentage points more likely to remain in San Francisco relative to the control group, indicating that a large share of the renters who remained at their 1994 address due to rent control would have left San Francisco had they not been covered by rent control. This would likely be viewed as a desirable outcome by rent control advocates.

We next analyze treatment effect heterogeneity along a number of dimensions.

We first find that our estimated effects are significantly stronger among older house- holds and among households that have already spent a number of years at their address prior to treatment. This is consistent with the idea that both of these popu- lations are less likely to experience personal shocks requiring them to change resi- dence and thus, are better able to take advantage of the potential savings offered by rent control.

We then examine whether the effects we estimate vary across racial groups. We do not directly observe race in our data, so we use an imputation procedure based on renters’ names and addresses.
2 We find rent control has an especially large impact
on preventing the displacement of racial minorities from San Francisco, suggesting that rent control helps to foster the racial diversity of San Francisco, at least among the initial cohort of renters covered by the law.

Finally, we analyze whether rent control enables tenants to live in neighborhoods with better amenities. One might expect neighborhoods with the largest increases in market prices and amenities would be ones where tenants would remain in their rent-controlled apartments the longest, since their outside options in the neighbor - hood would be especially expensive. However, for these same reasons, landlords in these high-rent, high-amenity neighborhoods would have large incentives to remove tenants.
3 They then could either reset rents to market rates with a new tenant or rede- velop the building as condos or new construction, both of which are exempt from rent control. These landlord incentives would push rent control tenants out of the nicest neighborhoods. In fact, we find the landlords’ incentives appear to dominate.

The average tenant treated by rent control lives in a census tract with worse observ- able amenities, as measured by the census tract’s median household income, share of the population with a college degree, median house value, and share unemployed. 2 We impute race by combining imputed race based on first and last name (Ye et al. 2017) and the racial mix of one’s census block of residence in 1990. See Section II for more details. 3 In practice, landlords use a number of legal means to remove their tenants, including owner move-in eviction, Ellis Act eviction, or monetary compensation. Landlords may also engage in various pressure tactics, such as tardy maintenance, to pressure tenants to leave.
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THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2019
Thus, while rent control does prevent displacement from San Francisco, it does not provide access to the best neighborhoods in the city.

The evidence above suggests that landlords do not passively accept the burdens of the law. To further study the landlord response to the rent control expansion and to understand the impact of rent control on rental supply, we merge in historical parcel history data from the San Francisco Assessor’s Office, which allows us to observe parcel splits and condo conversions. We find that rent-controlled build- ings were 8 percentage points more likely to convert to a condo or a Tenancy in Common (TIC) than buildings in the control group. Consistent with these findings, we find that rent control led to a 15 percentage point decline in the number of rent- ers living in treated buildings and a 25 percentage point reduction in the number of renters living in rent-controlled units, relative to 1994 levels. This large reduction in rental housing supply was driven by both converting existing structures to own- er-occupied condominium housing and by replacing existing structures with new construction.

This 15 percentage point reduction in the rental supply of small multi-family housing likely led to rent increases in the long run, consistent with standard eco- nomic theory. In this sense, rent control operated as a transfer between the future renters of San Francisco (who would pay these higher rents due to lower supply) to the renters living in San Francisco in 1994 (who benefited directly from lower rents). Furthermore, since many of the existing rental properties were converted to higher-end, owner-occupied condominium housing and new construction rent- als, the passage of rent control ultimately led to a housing stock which caters to higher income individuals. We directly test whether rent control led to in-migration of higher income residents by imputing household income as the per capita income of the census block groups in which the building occupants resided in five year prior.

We find that this high-end housing, developed in response to rent control, attracted residents with at least 18
percent higher income, relativ
e to control group buildings
in the same zip code.

Taking all of these points together, it appears rent control has actually contributed to the gentrification of San Francisco, the exact opposite of the policy’s intended goal. Indeed, by simultaneously bringing in higher income residents and prevent- ing displacement of minorities, rent control has contributed to widening income inequality of the city. For a full quantitative analysis of the welfare gains and losses due to rent control, see our companion paper (Diamond, McQuade, and Qian 2018), which estimates a dynamic discrete choice model of tenant migration and performs general equilibrium counterfactual analysis of the impacts of rent control.

Our paper is part of the literature on rent control. The two papers most closely related to ours are Sims (2007) and Autor, Palmer, and Pathak (2014), both of which study the effects of ending rent control in the Boston metropolitan area. Sims (2007) uses American Housing Survey (AHS) data to show that towns in the Boston metro- politan area in which rent control was abolished saw increases in rental supply and increased housing maintenance. Sims (2007) also shows some evidence of spillover effects on non-controlled properties. Autor, Palmer, and Pathak (2014) use proper - ty-level data on assessed values and transaction prices in Cambridge, Massachusetts to investigate these spillover effects more directly. They show that decontrol led to price appreciation at decontrolled and never-controlled units. 3369
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 Our paper is different on a number of important dimensions. First, our paper uses a different natural experiment which has the nice feature of generating qua- si-random assignment of rent control within narrowly defined neighborhoods. More substantively, by bringing to bear a unique, rich, and previously unused dataset, our paper is the first in this literature to be able to study how rent control impacts the behavior of the actual tenant beneficiaries. These estimates reveal a number of important insights regarding the value tenants place on rent control protections and rent control’s ability to limit displacement, but also potential limitations in the abil- ity of tenants to realize rent savings due to landlord responses.

Finally, since our unique data provide property-level information on renovations, condo conversions, and redevelopment, our paper shows that rent control can lead to an upgraded housing stock catering to higher income individuals. Indeed, the pre- vious literature has shown that ending rent control leads to higher maintenance and higher nearby property values. To reconcile these seemingly conflicting points, it is crucial to understand that decontrol studies the effects of removing rent control on buildings which still remain covered. In fact, one of our key points is to show that a large share of landlords substitute away from supply of rent-controlled housing, making those properties which remain subject to rent control a selected set. In this way, studying the introduction of rent control, which our paper does, is not the same as studying the abolishment of rent control.

There also exists an older literature on rent control combining applied theory with cross-sectional empirical methods. These papers test whether the data are consistent with the theory being studied, but usually cannot quantify causal effects of rent con- trol (Early 2000, Glaeser and Luttmer 2003, Gyourko and Linneman 1989, Gyourko and Linneman 1990, Moon and Stotsky 1993, Olsen 1972).

The remainder of the paper proceeds as follows. Section I discusses the history of rent control in San Francisco. Section II discusses the data used for the analysis.

Section III presents our empirical results. Section IV concludes.

I. A History of Rent Control in San Francisco
Regulations are widespread in housing markets, and rent controls are argu- ably among the most important historically (Stigler and Friedman 1946, Gyourko and Glaeser 2008). The modern era of US rent controls began as a part of World War
II era price controls and as a reaction to housing shortages following demo -
graphic changes immediately after the war (Fetter 2016). These “hard price con- trols” that directly regulate the exact price of housing have been replaced by newer policies that regulate rent increases (Arnott 1995). This “newer style” policy is what exists in San Francisco.

Rent control in San Francisco began in 1979, when acting Mayor Dianne Feinstein signed San Francisco’s first rent control law. Pressure to pass rent control measures was mounting due to high inflation rates nationwide, strong housing demand in San Francisco, and recently passed Proposition 13.
4 This law capped annual nom-
inal rent increases to 7 percent and
covered all rental units built before June 13,
4 Proposition 13, passed in 1978, limited annual property tax increases for owners. Tenants felt they were enti- tled to similar benefits in the form of capped annual rent increases. 3370
THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2019
1979 with one key exemption: owner-occupied buildings containing 4 units or less.5 These “mom and pop” landlords were cast as being less profit-driven than large- scale, corporate landlords, and more similar to the tenants being protected. These small multi-family structures made up about 44
percent of the rental housing stock
in 1990, making this a large e
xemption to the rent control law.

While this exemption was intended to target “mom and pop” landlords, in prac- tice small multi-families were increasingly purchased by larger businesses who would then sell a small share of the building to a live-in owner so as to satisfy the rent control law exemption. This became fuel for a new ballot initiative in 1994 to remove the small multi-family rent control exemption. This ballot initiative barely passed in November 1994. Suddenly, all multi-family structures with four units or less built in 1979 or earlier were now subject to rent control. These small multi-fam- ily structures built prior to 1980 remain rent-controlled today, while all of those built from 1980 or later are still not subject to rent control. San Francisco rent control laws have remained stable since then, possibly due to the statewide Costa-Hawkins Act. This law precludes any California city from rent controlling any housing stock built 1994 or later and regulates the scope of rent control allowed. For example, it requires rent-controlled apartment rents to be unregulated between tenants.

II. Data
We bring together data from multiple sources to enable us to observe property characteristics, determine treatment and control groups, track the migration deci- sions of tenants, and observe the property decisions of landlords. Our first dataset is from Infutor, which provides the entire address history of individuals who resided in San Francisco at some point between the years of 1980 and 2016. 6 The data
include not only individuals’ San Francisco addresses, but any other address within the United States at which that individual lived during the period of 1980–2016 . The
dataset pro
vides the exact street address, the month and year in which the individual lived at that particular location, the name of the individual, and some demographic information including age and gender.

We link these data to property records provided by DataQuick. These data pro- vide us with a variety of property characteristics, such as the use-code (single-fam- ily, multi-family, commercial, etc.), the year the building was built, and the number of units in the structure. For each property, the data also detail its transaction history since 1988, including transaction prices, as well as the buyer and seller names. By comparing last names in Infutor to the listed owners of the property in DataQuick, we are able to distinguish owners from renters.

Next, we match each address to its official parcel number from the San Francisco Assessor’s office. Using the parcel ID number from the Secured Roll data, we merge in any building permits that have been associated with that property since 1980.

These data come from the San Francisco Planning office. This allows us to track 5 The annual allowable rent increase was cut to 4 percent in 1984 and later to 60 percent of the CPI in 1992, where it remains today.
6 Infutor is a data aggregator of address data using many sources including sources such as phone books, voter files, property deeds, magazine subscriptions, credit header files, and others. 3371
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 large investments in renovations over time based on the quantity and type of permit issued to each building.

Finally, the parcel number also allows us to link to the parcel history file from the Assessor’s office. This allows us to observe changes in the parcel structure over time. In particular, this allows us to determine whether parcels were split off over time, a common occurrence when a multi-family apartment building (one parcel) splits into separate parcels for each apartment during a condo conversion.

Summary statistics are provided in Table 1. We see the average renter in our sam- ple in 1994 is about 37 years old and has lived at their current address for 6 years.

We also see that these small multi-family properties are made up of 82 percent
(0.74
/0.9) renters and 18 percent o
wner occupants prior to 1994.

A. Data Representativeness
To examine the representativeness of the Infutor data, we link all individuals reported as living in San Francisco in 1990 to their census tract, to create census tract population counts as measured in Infutor. We make similar census tract popu- lation counts for the year 2000 and compare these San Francisco census tract popu- lation counts to those reported in the 1990 and 2000 Census for adults 18 years old and above. Regressions of the Infutor populations on census population are shown in Figure 1.
7 Panel A shows that for each additional person recorded in the 1990 Census, Infutor contains an additional 0.44 people, suggesting we have a 44 percent sample of the population. While we do not observ
e the universe of San Francisco
residents in 1990, the data appear quite representative, as the census tract population in the 1990 Census can explain 69
percent of the census tract variation in population
measured from Infutor
. Our data are even better in the year 2000. Panel B shows that we appear to have 1.1 people in Infutor for each person observed in the 2000 US Census. We likely overcount the number of people in each tract in Infutor since we are not conditioning on year of death in the Infutor data, leading to overcounting of alive people. However, the Infutor data still tracks population well, as the census tract population in the 2000 Census can explain 90
percent of the census tract vari
-
ation in population measured from Infutor.

Infutor also provides information on age. As additional checks, we compare the population counts within decadal age groups living in a particular census tract as reported by Infutor to that reported by the Census. We again report the results for both 1990 and 2000. Unlike the prior analysis, we must drop Infutor observations missing birth date information for this, making our sample smaller. As shown in panel A of Table 2, the slopes of the regression lines for the 18–29, 30–39, 40–49, 50–59, and 60–69 age groups are 0.31, 0.44, 0.42, 0.24, and 0.16, respectively. This indicates the Infutor coverage is strongest for 30–49-year-olds in 1990. The R 2 val-
ues are also the highest in this age range at 65 to
76 percent . The co
verage of the
data improves dramatically by 2000, as shown in panel A of Table 2. The regression line slopes for the respective age groups are now 0.33, 0.74, 0.72, 0.70, 0.45. The R
2 values range from 0.61–0.85. It is clear the data disproportionately undersamples 7 We only can do data validation relative to the US Censuses for census tracts in San Francisco because we only have address histories for people who lived in San Francisco at some point in their life. 3372
THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2019
the youngest group, but this is unsurprising as these data come from sources such as credit header files, voter files, and property deeds. Eighteen-year-olds are less likely to show up in these sources right away. Overall the data coverage looks quite good.

As described above, we merge the Infutor data with public records information provided by DataQuick about the particular property located at a given address, such as use-code and age of the property. We assess the quality of the matching procedure by comparing the distribution of the year buildings were built across census tracts among addresses listed as occupied in Infutor versus the 1990 and 2000 Censuses.

Table 1—Sample Characteristics of Multi-Family Properties (2– 4 Units) and Their Tenants 1990–1993 1994–2016
Treat Control Difference Treat Control Difference
Panel A. Tenants living in multi-family residence (2−4 units) A1. Demographics
Age in 1993 37.708 37.120 0.587 37.708 37.120 0.587
(10.438
) (10.639) (0.247) 10.438 (10.639) (0.247)
A2. Residency
In San Francisco 0.954 0.954 0.000 0.569 0.538 0.032
(0.210
) (0.210) (0.002) (0.495) (0.499) (0.002)
Same address 0.870 0.867 0.003 0.261 0.240 0.021
(0.336
) (0.340) (0.004) (0.439) (0.427) (0.002)
Years at address 6.015

5.825 0.190 6.590 6.267 0.324
(3.958) (3.927) (0.047) (5.898) (5.530) (0.029)
Number of persons 44,502 1,861 46,363 44,502 1,861 46,363 Panel B. Multi-family properties (2−4 units)
B1. Residency
Con
version 0.000 0.000 0.000 0.096 0.044 0.051
(0.009) (0.000) (0.000) (0.294) (0.206) (0.002)
B2. Population, 1990–1994
Population
/avg. population 0.898 0.905 −0.008 2.282 2.252 0.030
(0.436) (0.426) (0.007) (4.029) (2.998) (0.028)
Renters
/avg. population 0.741 0.737 0.004 1.680 1.700 −0.020
(0.484) (0.482) (0.008) (3.555) (2.517) (0.025)
Renters in rent-controlled 0.741 0.737 0.004 1.404 1.570 −0.165 b
uildings/avg. population (0.484) (0.482) (0.008) (1.927) (2.053) (0.014) Renters in redev
eloped 0 0 0 0.129 0.060 0.069
b
uildings/avg. population (0) (0) (0) (0.740) (0.541) (0.005) Owners
/avg. population 0.157 0.168 −0.012 0.602 0.552 0.050
(0.329) (0.335) (0.006) (1.581) (1.348) (0.011)
B3. Permits
Cumulati
ve 0.072 0.088 −0.016 0.290 0.254 0.035
Add
/alter/repair per unit (0.231) (0.287) (0.004) (0.511) (0.536) (0.004) Number of parcels 25,925 892 26,817 25,925 892 26,817
Notes: Panel A reports the summary statistics of the demographic characteristics and residency outcomes during 1990–2016 of our tenant sample. The sample consists of all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2–4 units that were built during 1900–1990.

Panel B reports the summary statistics of the outcomes variables related to residency, population changes, and per- mit issuance during 1990–2016 of our property sample. The sample consists of all parcels that are multi-family residence with 2–4 units in San Francisco that were built during 1900−1990. The Treat and Control columns report the mean and standard deviation (in parentheses) of each outcome variable at the tenant level in panel A and at the property level in panel B. The Difference column reports the coefficient and standard error (in parentheses) of a regression of each outcome variable on the treatment dummy at the tenant level in panel A and at the property level in panel B.
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 If a building is constructed after 1993 according to its current day use-code, but we observe a person living there in 1993, we include it in the treatment group for rent control. Panel B of Table 2 shows the age distribution of the occupied stock by cen- sus tract. In both of the years 1990 and 2000, our R
2 values range from 67 percent
0 2,000
4,000
6,000
8,000Infutor population 18+
0 2,000 4,000 6,000 8,000
Census population 18+
Panel A. 1990 Infutor versus Census Panel B. 2000 Infutor versus Census 95% CI Fitted values
β = 0.442 (0.026)
R2 = 0.692
Observations = 127
β = 1.145 (0.029)
R
2 = 0.898
Observations = 175
0 2,000
4,000
6,000
8,000
10,000Infutor population 18+
0 2,000 4,000 6,000 8,000
Census population 18+
45-degree
Figure 1. Validation of Infutor Population versus US Census Population Notes: Plot shows the population of 18 and over in each census tract in 1990 and 2000 from Infutor data against that from 1990 and 2000 Censuses, respectively. The fitted line is by OLS.

Table 2—Representativeness of Infutor Data: Population by Age Groups and Age of Occupied Housing Stocks
1990 2000
Age group Slope SE R2 Age group Slope SE R2
Panel A. Population by age group
18–29 0.314 0.026 0.534 18–29 0.325 0.016 0.696
30–39 0.444 0.022 0.758 30–39 0.744 0.024 0.850
40–49 0.416 0.027 0.649 40–49 0.715 0.032 0.741
50–59 0.237 0.023 0.458 50–59 0.695 0.033 0.723
60–69 0.159 0.015 0.469 60–69 0.447 0.027 0.611
Panel B. Age of occupied housing
Year built Slope SE R2 Year built Slope SE R2
1970–1990 0.639 0.046 0.667 1980–2000 0.813 0.024 0.876 1950–1969 0.928 0.046 0.807 1960–1979 1.083 0.036 0.853 1940–1949 1.111 0.035 0.911 1950–1959 0.955 0.049 0.711 1939 or earlier 1.024 0.040 0.872 1940–1949 1.323 0.042 0.863 1939 or earlier 1.144 0.036 0.863
Notes: Panel A reports the coefficients, standard errors, and R 2 values of regressing the population counts within various age groups in each census tract from Infutor data against those from the Census in the year 1990 and 2000 respectively. Panel B reports the coefficients, standard errors, and R2 values of regressing the fraction of buildings built in various time periods in each census tract from Infutor data against those from the Census in the year 1990 and 2000 respectively. In panel B, the regressions are weighted by the number of occupied housing units in each census tract from the Census.
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to 91 percent and we often cannot reject a slope of 1.8 This highlights the extremely high quality of the linked Infutor-DataQuick data, as the addresses are clean enough to merge in the outside data source DataQuick and still manage to recover the same distribution of building ages as reported in both the 1990 and 2000 Censuses.

To measure whether Infutor residents were owners or renters of their properties, we compare the last names of the property owners list in DataQuick to the last names of the residents listed in Infutor. Since property can be owned in trusts, under a business name, or by a partner or spouse with a different last name, we expect to underclassify residents as owners. Figure 2 plots the Infutor measure of ownership rates by census tract in 1990 and 2000, respectively, against measures constructed using the 1990 and 2000 Censuses. In 1990 (2000), a 1 percentage point increase in the owner-occupied rate leads to a 0.43 (0.56) percentage point increase in the ownership rate measured in Infutor. Despite the undercounting, our cross-sectional variation across census tract matches the 1990 and 2000 censuses extremely well, with R
2 values over 90 percent in both decades. This further highlights the quality of the Infutor data.

Imputing Tenant Race
We use a two-step procedure to impute the r ace/
ethnicity of individuals in
our main sample of analysis: all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993. In the first step, we use NamePrism, a 8 Since year built comes from the Census long form, these data are based only on a 20 percent sample of the true distribution of building ages in each tract, creating measurement error that is likely worse in the census than in the merged Infutor-DataQuick data.
0 0.2

0.4 0.6

0.8 1
0 0.2

0.4 0.6

0.8 1
Infutor ownership rate
Panel A. 1990 Infutor-DataQuick versus Census Panel B. 2000 Infutor-DataQuick versus Census 0 0.2 0.4 0.6 0.8 1
Census ownership rate
Infutor ownership rate
0 0.2 0.4 0.6 0.8 1
Census ownership rate
95% CI Fitted values45-degree
β = 0.432 (0.014)
R2 = 0.910
Observations = 100
β = 0.562 (0.011)
R
2 = 0.939
Observations = 159
Figure 2. Home Ownership Rates in Infutor-DataQuick versus US Census Notes: Plot shows census tract average owner occupant rates in 1990 and 2000 from Infutor-DataQuick data verus that from 1990 and 2000 Censuses. The size of marker is proportional to the number of occupied housing units in each census tract. The fitted line is by weighted least squares. 3375
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 non-commercial ethnicity/nationality classification tool intended to support aca- demic research (Ye et al. 2017), to compute baseline probabilities of race/ethnicity for each tenant based on her first name and last name. In the second step, we use Bayes’ rule to update the name-based probabilities for race and ethnicity using the local racial distribution at each tenant’s place of residence in 1990, following a sim- ilar methodology used by the Consumer Financial Protection Bureau (CFPB 2014).

More details about each step are provided below.

In step 1, for each tenant, we use both her first and last name to query the NamePrism online tool and obtain baseline probabilities for the six ethnic categories defined by the US Census Bureau: Hispanic; non-Hispanic white;
non-Hispanic black or African

American; non-Hispanic Asian/Pacific Islander; non-Hispanic American Indian and Alaska Native; and non-Hispanic Multi-racial.9 NamePrism employs a training data- set of 57 million contact lists from a major internet company, US Census data on the distribution of last names by race, and trains its algorithm using the homophily principle exhibited in communication as the basis for its ethnicity classifier. 10 In this
step, each tenant is assigned a probability, ranging from 0 percent to 100 percent , of belonging to each of the six ethnic groups, and the six probabilities sum to 1.

In step 2, we update each tenant’
s baseline racial probabilities with the racial and
ethnic characteristics of the census block associated with her place of residence in 1990 using Bayes’ rule to obtain posterior probabilities for the six ethnic groups. 11 In particular, for a tenant with name s who
resides in geographic area g , we
calcu-
late the probability of race or ethnicity r for each of the six categories for a gi ven
name s , denoted as Pr (r | s) . From the Summary File 1 (SF1) from Census 1990, we obtain the proportion of the population belonging to race or ethnicity r that li ves in
geographic area g , denoted as Pr (g | r) . Bayes’ rule then gives the probability that a tenant with name s residing in geographic area g belongs to race or ethnicity r :

An assumption necessary for the
validity of the Bayesian updating procedure is that the probability of living in a given geographic area, given one’s race, is independent of one’s name. For example, it assumes that blacks with the name John Smith are just as likely to live in a certain neighborhood as blacks in general.

For each tenant, we then assign a final racial probability if the maximum of the six posterior probabilities is equal to or above

0.8 , and a final racial/
ethnic cate-
gory corresponding to the maximum posterior; otherwise a tenant’s race/ethnicity is unclassified. Table 3 shows the breakdown of our racial and ethnic classification for our main sample of analysis.
9 This classification considers Hispanic as mutually exclusive from the race categories, with individuals identi- fied as Hispanic belonging only to that category, regardless of racial background. 10 People tend to communicate more frequently with others of similar age, language, and location. 11 In practice, census block level information on the racial and ethnic composition is available for 94.7 percent of our sample. F
or the rest of sample, we use racial and ethnic composition at the census block group ( 4 percent ), census
tract ( 0.2 percent ),
and 5-digit zip code levels ( 1 percent ),
whichever one is first available in the order listed.

We set the posterior probabilities equal to the baseline probabilities from NamePrism for the rest: 0.1 percent of our sample.
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Our methodology is similar to what’s used by the CFPB to construct proxy con- sumer race in order to conduct fair lending analysis. CFPB (2014) and Elliott et al. (2009) demonstrate that combining geography- and name-based information into a single proxy probability for race/ethnicity significantly outperforms traditional classification methods based on names or geography alone. The key difference between our method and CFPB’s method is that we use NamePrism to compute “prior” probabilities, whereas CFPB relies on the racial distribution for common last names in the United States published by the Census Bureau (Comenetz 2016). Since NamePrism uses both first and last names from a much larger name database, it is able to classify race/ethnicity for a much wider range of names at higher accuracy.

Moreover, we use census block level racial composition for Bayesian updating of racial probabilities whenever possible, whereas CFPB uses racial distribution at the census block group level, which is a larger geographic unit, and thus less refined.

Validation of Race Imputation.—We report some summary statistics regard- ing our race imputation methodology and perform a few validation checks. Using our imputation procedure and the linked Infutor-DataQuick data, we first report in column 5 of Table 3 the racial distribution of all tenants aged 20–65 living in multi-family residences with 2–4 units as of December 31, 1993. Column 6 of T
able 3 reports the 1990 Census measure of this distribution.

As in the census, we
find that Asians are the most numerous minority, followed by Hispanics and then blacks. This table also shows that our procedure somewhat overrepresents whites in San Francisco and underrepresents the number of minorities. This is because we only assign a race to an individual if the probability of that race is above 80 percent.

In practice, this means 8,009 tenants are not assigned a race, equal to 17.27 percent of our tenant sample. Man
y of these unassigned individuals are likely minorities, as a large fraction of the unassigned are those with minority-sounding names but who live in relatively racially integrated neighborhoods.
12 12 If we do not impose this cutoff and instead simply calculate raw means of each racial group’s probabilities, our racial distribution looks much closer to the distribution reported by the Census. We feel that imposing the cutoff is appropriate, however, since it ameliorates concerns regarding measurement error in our regression analysis by Table 3—2010 Census Block Racial Distribution by Tenants’ Race among 1994 Rent Control Cohort Average share in 2010 census block SF overall
White Black Hispanic Asian Sample share 1990 census 2010 census (1) (2) (3) (4) (5) (6) (7)
Predicted race
White 63.4

4.2 12.1

16.4 75.01 57.36 52.26
Black 24.8 24.0 24.4 22.8 1.40 7.72 4.69
Hispanic

33.7 6.3 31.4

24.9 8.20 14.18

18.28 Asian 38.1 4.1 13.2

40.8 15.39 20.16 24.51
Notes: Sample consists of all tenants with a classified race/ethnicity between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2–4 units that were built during 1900–1990.

We geocode the 2010 addresses of tenants in our sample to the census block level. Columns 1–4 report the average shares of white, black, Hispanic, and Asian population in the census blocks containing the 2010 addresses of tenants in each classified racial/ethnic category. Column 5 reports the share of our sample by predicted race. Columns 6 and 7 report the share of tenants in San Francisco between 20 and 65 years old who were living in small multi-family
residences by racial
/ethnic categories according to the 1990 and 2010 US censuses. 3377
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 To further validate our methodology, we examine the average racial makeup of the 2010 census block in which our assigned individuals live. Note that this is an out-of-sample check since we use an individual’s 1990 address, not their 2010 address, in our imputation procedure. The results are reported in columns 1 through 4 of Table 3. Consistent with what one would expect from some degree of contin- ued racial sorting, individuals we classify as white live in neighborhoods with the greatest fraction of whites (as of 2010), those we classify as black live in neighbor- hoods with the greatest fraction of blacks (as of 2010), and similarly for Hispanics and Asians. The same sorting result appears when we regress racial shares of an individual’s 2010 census block on the individual’s assigned race. The results are reported in online Appendix Table
A2, with black being the omitted cate
gory. For
example, being white is the strongest positive predictor of the 2010 white share, being Hispanic is the strongest positive predictor of the 2010 Hispanic share, and similarly for Asians and blacks.

III. Empirical Results
Studying the effects of rent control is challenged by the usual endogeneity issues.

The tenants who choose to live in rent-controlled housing, for example, are likely a selected sample. To overcome these issues, we exploit the successful 1994 ballot initiative which removed the original 1979 exemption for small multi-family hous- ing of four units or less, as discussed in Section I.

In 1994, as a result of the ballot initiative, tenants who happened to live in small multi-family housing built prior to 1980 were, all of a sudden, protected by statute against rent increases. Tenants who lived in small multi-family housing built 1980 and later continued to not receive rent control protections. We therefore use as our treatment group those renters who, as of December 31, 1993, lived in multi-family
b
uildings of less than or equal to four units, built between years 1900 and 1979.

We use as our control group those renters who, as of December 31, 1993, lived in multi-family buildings of less than or equal to four units, built between the years of 1980 and 1990. We exclude those renters who lived in small multi-family buildings constructed post-1990 since individuals who choose to live in new construction may constitute a selected sample and exhibit differential trends. We also exclude tenants who moved into their property prior to 1980, as none of the control group buildings would have been constructed at the time.

When examining the impact of rent control on the parcels themselves, we use small multi-family buildings built between the years of 1900 and 1979 as our treat- ment group and buildings built between the years of 1980 and 1990 as our control group. We again exclude buildings constructed in the early 1990s to remove any differential effects of new construction. Figure 3 shows the geographic distribution of treated buildings and control buildings in San Francisco. Since our control group was built over a narrow time span, the sample size of the treatment group is much larger than the control group. However, the control group buildings cover many restricting to those individuals whose racial classification is more precise. We investigate using the entire sample as a robustness check in the online Appendix.
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neighborhoods across San Francisco, giving the treatment and control samples good overlap.

We next estimate balance tests between our treatment and control samples to evaluate whether rent control status was as good as randomly assigned. Table 1 compares the characteristics of tenants in treatment and control buildings, from 1990–1993,
prior to treatment. The comparisons in raw means do not control for the zip code of the building, which we will always condition on in our analysis. Panel A shows that tenants in the treated buildings are 0.6 years older than tenants in control buildings. This is unsurprising as the older buildings have been around much longer, allowing for longer tenancies and thus older residents. Indeed, we also see that the average tenant in the treatment building has lived there for 6 years prior to treatment, while control group tenants have lived there for 5.8 years. To account for this dif- ferences, we will always condition on the length of tenancy, measured at the time of treatment, when comparing treatment and control groups in the following analysis.

We begin our analysis by studying the impact of rent control provisions on its tenant beneficiaries. Policy advocates argue that tenants covered by rent control will be dramatically helped by lower housing costs, thereby enabling them to stay in communities that they have lived in for a number years and grown attached to. We Rent-controlled sample
Uncontrolled sample
Others
Figure 3. Geographic Distribution of Treated and Control Buildings in San Francisco Notes: The purple dots represent parcels in the treatment group, which are parcels corresponding to multi-family residences with 2–4 units in San
Francisco that were built between 1900–1979. The green dots represent parcels in the control group, which are parcels corresponding to multi-family residences with 2–4 units in San Francisco that were built between 1980–1990.

The gray dots represent other types of housing stocks such as single-family resi- dences and multi-family residences with five or more units. 3379
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 evaluate these claims first by quantifying rent control’s impact on the initial cohort of tenants living in the properties newly covered by the law. Later, in Section IIIB we examine ho
w landlords’ responses to the law change impacted the long-run housing supply of rental properties. In light of these findings, we then return to and evaluate the claim that rent control helps tenants by lowering housing costs and preventing displacement.

A. Tenant Effects
We first examine whether rent control “locks tenants into their apartments,” extending the duration of time they live at the address where they were first covered by rent control. On the one hand, locking tenants into their apartments could be viewed as a cost of rent control. Tenants might not be able to move to different types of housing as their needs change, such as when they get married or have a child. On the other hand, if tenants’ lack of migration not only keeps them in the same apart- ment but enables them to stay in San Francisco overall, then this could be viewed as a success in that rent control prevents displacement.

To evaluate these effects we use a difference-in-differences design described above, with the following exact specification:
(1)
Y iszt = δ zt + α i + β t T i + γ st + ϵ it .

Here, Y iszt are outcome variables equal to 1 if, in year t , the tenant i is still living at either the same address as the
y were at the end of 1993, or, alternatively, if the
tenant is still living in San Francisco. The variables α i denote individual tenant fixed effects. The variable T i denotes treatment, equal to 1 if, on December 31, 1993, the tenant is living in a multi-family building with less than or equal to four units built between the years 1900 and 1979.

We include fixed effects γ st denoting the interaction of dummies for the year s the tenant mo
ved into their 1993 apartment with calendar year t time dummies. These additional controls are needed since older b
uildings are mechanically more likely
to have long-term, low-turnover tenants; not all of the control group buildings were built when some tenants in older buildings moved in. Finally, note we have included a full set of zip-code-by-year fixed effects,
δ zt . In this way, we control for any dif-
ferences in the geographic distribution of treated buildings versus control buildings, ensuring that our identification is based off of individuals who live in the same neighborhood, as measured by zip code. Our coefficient of interest, quantifying the effect of rent control on future residency, is denoted by β t .

Our estimated effects are sho
wn in Figure 4, along with 90 percent confidence
intervals.

As further evidence of random assignment, we see no pre-trends leading up to time of treatment. Exactly at time of treatment we see a large spike in the probability that the treatment group remains at their 1993 address, versus the control group. We can see that tenants who receive rent control protections are persistently more likely to remain at their 1993 address relative to the control group. This effect decays over time, which likely reflects that as more years go by, all tenants are increasingly likely to move away from where they lived in 1993. Further, we find that treated tenants are also more likely to be living in San Francisco. This result 3380
THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2019
indicates that the assignment of rent control not only impacts the type of property a tenant chooses to live in, but also their choice of location and neighborhood type.

These figures also illustrate how the time pattern of our effects correlates with rental rates in San Francisco.13 We would expect our results to be particularly strong in those years with quickly rising rents and thus large potential savings. Along with our yearly estimated effect of rent control, we plot the yearly deviation from the log trend in rental rates against our estimated effect of rent control in that given year. We indeed see that our effects grew quite strongly in the mid- to late-1990s in conjunc- tion with quickly rising rents, relative to trend. Our effects then stabilize and slightly decline in the early 2000s in the wake of the dot-com bubble crash, which led to fall- ing rental rates relative to trend. Overall, we measure a correlation of 49.4 percent
between our estimated same address effects and median rents, and a correlation of

78.4 percent between our estimated SF ef
fects and median rents.

In Table 4, we collapse our estimated effects into a short-term 1994–1999 effect, a medium-term 2000–2004 effect, and a long-term post-2005 effect. We find that in the short run, tenants in rent-controlled housing are 2.18 percentage points more likely to remain at the same address. This estimate reflects a 4.03 percent increase
relati
ve to the 1994–1999 control group mean of 54.10 percent. In the medium term, rent-controlled tenants are 3.54 percentage points more likely to remain at the same address, reflecting a 19.38
percent increase ov
er the 2000–2004 control group mean
of 18.27 percent. Finally
, in the long term, rent-controlled tenants are 1.47 percent- age points more likely to remain at the same address. This is a 12.95 percent increase o
ver the control group mean of 11.35 percent. Whether these ef fects should widen
13 Annual advertised rents from the San Francisco Chronicle and Craigslist have been collected by Eric Fischer (https://github.com/ericfischer/housing-inventory/). Since we do not have the microdata, this gives us an aggregate San Francisco-wide annual time series of rents. Given that these data are based on actual listings, this is likely the most accurate measure of true market rate rents, among all possible data sources.

Treatment effect: same address
Panel A. Staying at the same address Panel B. Staying in San Francisco 1990 1995 2000 2005 2010 2015
Corr = 0.494
−0.2
−0.1
0 0.1

0.2 0.3

0.4 0.5
log median rent (detrended)
−0.2
−0.1
0 0.1

0.2 0.3

0.4 0.5
log median rent (detrended)
−0.02
0 0.02

0.04 0.06
−0.02
0 0.02

0.04 0.06
Treatment effect: In SF1990 1995 2000 2005 2010 2015
Corr = 0.784
In SF Real log median rent (detrended)Same address
Real log median rent (detrended)
Figure 4. Treatment Effect for Tenants in Multi-Family Residence ( 2–4 Units) Notes: Sample consists of all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2–4 units that were built during 1900 –1990 . The solid line plots the treatment
ef
fects for staying at the same address in panel A and staying in San Francisco in panel B along with 90 percent CI in shaded area.

The dotted line plots the yearly deviation from the log trend in median rental rates. Standard errors are clustered at the person level.
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 or narrow over time is ambiguous. On one hand, the wedge between market rate rents and rent control rents diverge, the longer one remains at one’s rent-controlled address. On the other hand, the mismatch between one’s 1993 address and the ideal location and type of housing is likely to grow over time, pushing tenants to give up their rent control. Since our long-term results are smaller than our medium-term findings, it appears the mismatch effect begins to grow faster than the below market rent effect over the medium to long term.

Tenants who benefit from rent control are 2.00 percentage points more likely to remain in San Francisco in the short-term, 4.51 percentage points more likely in the medium-term, and 3.66 percentage points more likely in the long term. Relative to the control group means, these estimates reflect increases of 2.62 percent, 8.78 per-
cent, and

8.42 percent, respecti
vely. Since these numbers are of the same magnitude
as the treatment effects of staying at one’s exact 1993 apartment, we find that absent rent control a large share of those incentivized to stay in their apartments would have otherwise moved out of San Francisco. Since most of the tenants “locked” into their apartments by rent control would have otherwise left the city rather than select a different apartment in the same neighborhood, the allocative inefficiency effects of rent control might be smaller than its impacts on preventing displacement.

Robustness.—A key identifying assumption for our analysis is that once neigh- borhood characteristics have been controlled for, as well as the number of years lived in the apartment as of December 31, 1993, those living in older versus newer buildings would not exhibit differential trends in migration. As a robustness test, in panel A of Table 5, we have restricted our treatment group to individuals who lived in structures built between 1960 and 1979, thereby comparing tenants in buildings built slightly before 1979 to tenants in buildings built slightly after 1979. We find statistically indistinguishable results from our main analysis, with point estimates actually 5
percent to 63 percent lar
ger across the six point estimates.

Table 4—Treatment Effect for Tenants of Multi-Family Residence (2–4 Units) In SF Same address
(1) (2)
Treat × period
1994–1999 0.0200 0.0218
(

0.0081) (0.0083)
2000–2004 0.0451 0.0354
(

0.0115) (0.0088)
Post 2005 0.0366 0.0147
(0.0109
) (0.0063)
Control mean, 1994–1999 0.7641 0.5410
Control mean, 2000–2004 0.5138 0.1827
Control mean, post-2005 0.4346 0.1135
Adjusted R 2 0.586 0.608
Observations 1,251,801
1,251,801
Notes: Sample consists of all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2–4 units that were built during 1900–1990. Table reports the mean of dependent variables for the control group during 1990–1994, 2000–2004, and post-2005. Standard errors are clustered at the person level. 3382
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As further robustness, we redefine the neighborhood more finely, using census tracts instead of zip codes. Panel B of Table 5 repeats the analysis using census tract by year fixed effects. The results are also statistically indistinguishable from our main results, although the point estimates are between 1 percent and 28 percent
smaller across the six point estimates. Dropping the zip-code-by-year fixed ef fects
also produces similar results.

As a final robustness check, we use an alternative control group of renters living in larger multi-family apartment buildings not subject to rent control. Specifically, we create a control group of renters living in buildings with between 5 and 10 apart- ment units built between 1980 and 1990. We exclude large multi-family buildings built prior to 1980 from the control group because they have been covered by rent Table 5—Robustness Checks: Treatment Effect for Tenants of Small Multi-Family Residences Panel A. Treatment group:
buildings built between 1960
and 1979
Panel B. Census tract fixed
effects
In SF Same address In SF Same address
(1) (2) (3) (4)
Treat × period
1994–1999 0.0326

0.0289 0.0175 0.0157
(0.0105) (0.011) (0.0084) (0.0087)
2000–2004 0.0642

0.0370 0.0426 0.0284
(0.0151) (0.0118) (0.012) (0.0092)
Post-2005 0.0531

0.0164 0.0364 0.0113
(0.0145) (0.0084) (0.0114) (0.0066)
Control mean, 1994–1999 0.7641 0.541 0.7641 0.541
Control mean, 2000–2004 0.5138 0.1827 0.5138 0.1827
Control mean, post-2005 0.4346 0.1135 0.4346 0.1135
Adjusted R2 0.584 0.609 0.588 0.609
Observations 135,594 135,594 1,243,242 1,243,242
Panel C. Control group lives
in buildings with 5–10 units
Panel D. Control group lives
in buildings with 2–10 units
Treat × period
1994–1999 0.0319

0.0162 0.0256 0.0201
(0.0096) (0.0094) (0.0063) (0.0064)
2000–2004 0.0424

0.0291 0.0452 0.0340
(0.0132) (0.0099) (0.0089) (0.0067)
Post-2005 0.0400

0.0167 0.0387 0.01575
(0.0124) (0.0071) (0.0084) (0.0048)
Control mean, 1994–1999 0.7356 0.541 0.7507 0.541
Control mean, 2000–2004 0.4935 0.178 0.5043 0.1805
Control mean, post-2005 0.4092 0.1064 0.4227 0.1101
Adjusted R2 0.587 0.608 0.587 0.608
Observations 1,246,023 1,246,023 1,296,270 1,296,270
Notes: In panel A, we change our tenant sample to all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2–4 units that were built during 1960–1990. Hence, we have restricted our treatment group to individuals who lived in buildings built between 1960 and 1979. In panel B, the sample of tenants is the same as in our baseline regressions. Instead of using zip-code-by-year fixed effects in our baseline regressions, we use census tract by year fixed effects. In panel C, we have changed our control group to individuals who lived in multi-family residences with 5–10 units that were built during 1980–1990. The treat- ment group is the same as in our baseline regressions. In panel D, we have changed our control group to individu- als who lived in multi-family residences with 2–10 units that were built during 1980–1990. The treatment group is the same as in our baseline regressions. Table reports the mean of dependent variables for the control group during 1990–1994, 2000–2004, and post-2005. Standard errors are clustered at the person level. 3383
DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 control since 1979. Using residents of these slightly larger buildings built in the 1980s should also act as a valid control group if the sorting of tenants to buildings within neighborhoods did not depend on the exact number of units in the build- ings. Panel C of Table 5 reports the treatment effect using this alternative control group. The effects are statistically indistinguishable from our main effects. Panel D of Table 5 combines our control groups, creating a larger control group of renters living in buildings with two to ten apartments building in the 1980s. Unsurprisingly, these effects are also statistically indistinguishable from our main estimates, but the standard errors are smaller due to the increased sample size of our control group.

Treatment Effect Heterogeneity.—These estimated overall effects mask economi- cally interesting heterogeneity. We begin by repeating our analysis separately within each racial group. Racial minorities may face discrimination in the housing market, indicating that rent control may be especially impactful on limiting their displace- ment. Figure 5 shows the treatment effects of remaining in one’s 1993 address for whites, and then the differential effects for each racial group. Since our sample sizes within any given racial group are smaller, we will focus on the overall “post” impact of rent control, not separating out the short-, medium-, and long-term effects.

Whites are 2.1 percentage points more likely to remain at their treated address due to rent control. For both blacks and Hispanics, we find larger treatment effects of 10.7 and 7.1 percentage point increases for these groups, respectively. 14 This suggests
these minority groups disproportionately valued rent control. In contrast, the effect for Asians is statistically indistinguishable from the whites effect, with a point esti- mate of 0.9 percentage points.

We see further evidence that racial minorities disproportionately benefited from rent control when looking at the impact of the law on remaining in San Francisco.

Rent control leads treated whites to be 2.8 percentage points more likely to remain in San Francisco, while blacks, Hispanics, and Asians are 10.7, 10.1, and 6.4 per - centage points more likely to remain in San Francisco, respectively. 15 This suggests
that rent control had a substantial impact on limiting displacement of minorities from the city, an additional sign that rent control strongly benefits the initial cohort of renters who are covered by the law.

We next examine treatment effect heterogeneity across neighborhoods, duration of tenancy, and age.
16 The goal of this exercise is two-fold. First we want to examine whether tenants who have lived in their neighborhoods for a long time disproportion- ately value rent control, as would be expected if these long-term tenants had built up a stock of neighborhood-specific capital. Second, we want to examine whether the value of rent control varies across tenant age. It is well known that younger individ- uals move more often. If young people need to move often for personal reasons, it 14 Since our sample of blacks is quite small, the differential effects for blacks are not statistically indistinguish- able from whites.
15 As a robustness check, we repeat this analysis on the entire sample, including the renters whose probabilities for their most likely imputed race were below 80 percent. These results are in online Appendix Figure A1. The result are statistically indistinguishable from our main results, but the differences in the point estimates across races are smaller. This is consistent with the fact we have much more measurement error in the imputed races for these additional renters.
16 We do not cut on race here as well, as the samples would become too thin. 3384
THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2019
will be hard for them to benefit from rent control since they cannot stay in one place long enough to access the insurance value of rent control.

To examine these effects, we cut the data by age, sorting individuals into two groups, a young group who were aged 20–39 in 1993 and an old group who were aged 40–65 in 1993. We also sort the data based on the number of years the indi- vidual has been living at their 1993 address. We create a “short-tenure” group of individuals who had been living at their address for less than four years and a “long-tenure” group of individuals who had been living at their address for between 4 and 14 years. Finally, we cut the sample of zip codes based on whether their housing price appreciation from 1990 to 2000 was above or below the median, as measured by the housing transactions observed in DataQuick. Ideally, we would measure market rental price appreciation across neighborhoods, but no data source for this exists. While rents and house prices need not be perfectly correlated, house prices and market rents tend to move together. We form eight subsamples by taking the
2 × 2 × 2 cross across each of these three dimensions and re-estimate our ef fects
for each subsample.

The results are reported in Table 6 and plotted in online Appendix Figures A2 and A3. We summarize the key implications. First, we find that the effects are weaker for younger individuals. We believe this is intuitive. Younger households are more likely to face larger idiosyncratic shocks to their neighborhood and housing prefer- ences (such as changes in family structure and employment opportunities), which makes staying in their current location particularly costly, relative to the types of shocks older households receive. Thus, younger households may feel more inclined to give up the benefits afforded by rent control to secure housing more appropriate for their circumstances.

Moreover, among older individuals, there is a large gap between the estimated effects based on tenure duration. Older, long-tenure households have a strong, pos- itive response to rent control. That is, they are more likely to remain at their 1993 Panel A. Staying at same address

0.021

0.086

0.05 −0.012

0.0031
White
Differential effect: black
Differential effect: Hispanic
Differential effect: Asian
Differential effect: minorities
−0.05 0 0.05 0.1 0.15 0.2
Same address
Panel B. Staying in San Francisco

0.028

0.079

0.073

0.036

0.045
White
Differential effect: black
Differential effect: Hispanic
Differential effect: Asian
Differential effect: minorities
−0.1 0 0.1 0.2 0.3
In SF
Figure 5. Heterogeneity by Tenant’s Race in Treatment Effect for Tenants Notes: Sample consists of all tenants with a classified race/ethnicity between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2−4 units that were built during

1900−1990 . For white tenants, we report the a
verage treatment effect in the post-1994 period along with 90 percent CI. For the other ethnic cate
gories, we report the differential treatment effect in the post-1994 period between white and each ethnic category along with 90 percent CI. Minorities consist of all ethnic groups other than white. Standard errors are clustered at the person lev
el.
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 address relative to the control group. In contrast, older, short-tenure individuals are estimated to have a weaker response to rent control. They are less likely to remain at their 1993 address relative to the control group.

To further explore the mechanism behind this result, we now investigate these effects based on the 1990–2000 price appreciation of their 1993 zip codes. Among older, long-tenure individuals, we find that the effects are always positive and stron- gest in those areas which experienced the most price appreciation between 1990 and 2000, as one might expect. For older, short-tenure households, however, the results are quite different. For this subgroup, the effects are actually negative in the areas which experienced the highest price appreciation. They are positive in the areas which experienced below-median price appreciation.
17 This result suggests that landlords actively try to remove tenants in those areas where rent control affords the most benefits, i.e., high price appreciation areas. There are a few ways a landlord could accomplish this. First, landlords could try to legally evict their tenants by, for example, moving into the properties themselves, known as owner move-in eviction. Alternatively, landlords could evict tenants according to the provisions of the Ellis Act, which allows evictions when an owner wants to remove units from the rental market: for instance, in order to convert the units into condos or a tenancy in common.
18 Finally, landlords are legally allowed to negotiate with tenants over a monetary transfer convincing them to leave. In this way, tenants may “bring their rent control with them” in the form of a lump sum tenant buyout. Of 17 A similar pattern holds for younger individuals as well, although the results are weaker. 18 Asquith (2018) studies the use of Ellis Act evictions in the 2000s by landlords of rent-controlled properties in San Francisco.

Table 6—Heterogeneity by Age, Tenure, and Neighborhood House Price Appreciation in Treatment Effect of Staying at Same Address Older tenants Younger tenants
(1) (2) (3) (4)
Panel A. Above-median house price appreciation zip codes Treat × post 0.062 −

0.107 0.018 −0.003
(0.019) (0.042) (0.012) (0.032)
Tenant tenure duration Long Short Long Short
Panel B. Below-median house price appreciation zip codes Treat
× post 0.041

0.010 0.007 0.039
(0.015) (0.033) (0.009) (0.018)
Tenant tenure duration Long Short Long Short
Notes: Sample consists of all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2– 4 units that were built during 1900–1990.

We first divide individuals into two groups by whether their 1993 zip code experi- enced above- or below-median house price appreciation during 1990–2000. We further sort the sample by age group. The young group refers to residents who were aged 20–39 in 1993 and the old group are residents who were aged 40–65 in 1993. Finally, we cut the data by number of years the individual has been living at their 1993 address. We define a long-tenure group of individuals who had been living at their 1993 address for greater than or equal to four years and a short-tenure group of individuals who had been living at their address for less than four years. The coefficients represent average treatment effects in the post-1994 period. Standard errors are clustered at the person level. See online Appendix Figures A2 and A3 that plot the full dynamics of these treatment effects.
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course, if landlords predominantly use evictions, tenants are not compensated for their loss of rent protection, weakening the insurance value of rent control.

Effects on Neighborhood Quality.—The results from the previous subsection help to rationalize some additional, final findings. In panel A of Figure 6, we exam- ine the impact that rent control has on the types of neighborhoods in which tenants live. We find that those who received rent control ultimately live in census tracts with lower house prices, lower median incomes, lower college shares, and higher unemployment rates than the control group. As panel B shows, this is not a func- tion of the areas in which treated individuals lived in 1993. In this figure, we fix Figure 6. Treatment Effect on Neighborhood Quality for Tenants of Multi-Family Residence ( 2−4 Units )
Notes: Sample consists of all tenants between 20 and 65 years old living in San Francisco as of December 31, 1993 and in multi-family residences with 2–4 units that were built during 1900–1990 . Median household income, share of residents with college education and abo
ve, median house value, and share of unemployed are measured in the census tract that an individual is living in a given year. The data sources are decennial censuses in 1990 and 2000, as well as 5-year pooled ACS for 2010 to 2013. Panel A plots the true treatment effects for various proxies of neigh- borhood quality. Panel B plots the placebo treatment effects where we assume those treated by rent control remain at their 1993 addresses, but allow the control group to migrate as seen in the data. The treatment effects along with

90 percent CI are plotted. Standard errors are clustered at the person lev el.
−4,000
−3,000
−2,000
−1,000
0 −0.03
−0.02
−0.01
0 −30,000
−20,000
−10,000
0 10,000
0 0.002

0.004

0.006
1990 2000 2010201120122013 1990 2000 2010201120122013
Median household income
Panel A. True treatment effect Panel B. Placebo treatment effect Share college
Median house value
Share unemployed
Median household income
Share college
Median house value
Share unemployed
−4,000
−2,000
0 2,000
0 0.02

0.04 0.06
0 50,000
100,000
150,000
−0.01
−0.005
0 0.005
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 the location of those treated by rent control at their 1993 locations, but allow the control group to migrate as seen in the data. If rent-controlled renters were equally likely to remain in their 1993 apartments across all locations in San Francisco, we would see the sign of the treatment effects on each neighborhood characteristic to be the same as in the previous regression. Instead, we find strong evidence that the out-migration of rent-controlled tenants came from very selected neighborhoods.

Had treated individuals remained in their 1993 addresses, they would have lived in census tracts which had significantly higher college shares, higher house prices, lower unemployment rates, and similar levels of household median income relative to the control group.

This evidence is consistent with the idea that landlords undertake efforts to remove their tenants or convince them to leave in improving, gentrifying areas. In addition, the rent control tenants are more likely to remain at their address within the less gentrifying areas, as we saw in the previous analysis in Table 6. These combined effects lead tenants treated by rent control to live in lower quality areas. Further, it highlights that rent control does not appear to be an effective means of providing tenants access to neighborhoods with better amenities. The better locales are where landlords have the most to gain from removing rent-controlled tenants and these landlords apparently work hard to make this happen. Having said that, our prior results did show that rent control helped tenants remain in San Francisco overall.

Thus, while they are unable to live in the nicest parts of the city, it is possible that by being able to remain in San Francisco, they are able enjoy lower commute times or work at better jobs than they otherwise would have had they been displaced. These types of amenities cannot be observed in our data.

B. Parcel and Landlord Effects
The results above strongly suggest that while tenants value and take advantage of the protections offered by rent control, landlords actively take steps to reduce the burdens of the law, especially in those areas in which it would be most profitable to do. Motivated by these findings, in this section, we continue our analysis by studying and quantifying the landlord response more directly. To do so, we exam- ine the impact of rent control on the properties themselves. In particular, we study how rent control affects the type of residents who live in the buildings, as well as how it impacts the investments that landlords choose to make in the properties. This analysis will enable us to understand the effects of rent control on long-term rental housing supply. Such changes in housing supply will ultimately impact equilibrium market rents and thus housing affordability for future renters.

Summary statistics for our key outcomes are in panel B of Table 1. This table shows that treatment and control properties are balanced in the pre-period in terms of total residents and number of renter residents. We see 1.2 percentage points more owners in the control group and 1.6 percentage points more construction/renova- tion permits. These small differences reflect that fact that the control buildings are slightly newer.

We run a specification similar to (1):
(2)
Y kzt = δ zt + λ k + β t T k + ϵ kt ,
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where k now denotes the individual parcel and λ k represent parcel fixed effects. The variable T k denotes treatment, equal to one if, on December 31, 1993, the parcel is a multi-family building with less than or equal to four units built between the years 1900 and 1979. The δ zt variables once again reflect zip-code-by-year fixed effects.

Our outcome variables Y kzt now include the number of renters and owners living in the building, the number of renovation permits associated with the building, and whether the building is ever converted to a condo or TIC. The permits we look at specifically are addition/alteration permits, taken out when major work is done to a property.

We begin by plotting in panel A of Figure 7 the effects of rent control on the number of individuals living at a given parcel, calculated as a percentage of the average number of individuals living at that parcel between the years 1990–1994.

We estimate a decline of approximately 6.4
percent ov
er the long run, although this
effect is not statistically significant.

We next decompose this effect into the impact on the number of renters and the number of owners living at the treated buildings. As shown in panel B, we find that there is a significant decline in the number of renters living at a parcel, equal to

14.5 percent in the late 2000s, relativ
e to the 1990–1994 level. Panel C shows that
the decline in renters was counterbalanced by an increase of 8.1 percent in the num- ber of o
wners in the late 2000s. This is our first evidence suggestive of the idea that landlords redeveloped or converted their properties so as to exempt them from the new rent control regulations.

We now look more closely at the decline in renters. In panel A of Figure 8, we see that there is an eventual decline of 24.6
percent in the number of renters li
ving
in rent-controlled apartments, relative to the 1990–1994 average. 19 This decline is significantly larger than the overall decline in renters. This is because a number of buildings which were subject to rent control status in 1994 were redeveloped in such way so as to no longer be subject to it. These redevelopment activities include tearing down the existing structure and putting up new single family, condominium, or multi-family housing or simply converting the existing structure to condos. These redeveloped buildings replaced 7.2
percent of the initial rental housing stock treated
by rent control, as shown in panel B of Figure 8.

T
o further investigate this mechanism, we check directly whether a multi-family property which fell under the rent control regulations in 1994 is more likely to have converted to condominium housing or a tenancy in common, relative to a multi-fam- ily property which did become subject to rent control. In panel C of Figure 8, we show that treated buildings are 8 percentage points likely to convert to condo or TIC in response to the rent control law. This represents a significant loss in the supply of rent-controlled housing.

As a final test of whether landlords actively respond to the imposition of rent control, we examine whether the landlords of rent-controlled properties dispro- portionately take out addition/alteration (i.e., renovation) permits. We find this to strongly be the case, with treated buildings receiving 4.6 percent more addition/
alteration permits per unit as sho
wn in panel D of Figure 8. Of course, conversions
19 Note here that we mean relative to the number of individuals who lived at parcels which received rent control status due to the 1994 law change.
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 of multi-family housing to condos undoubtedly require significant alteration to the structural properties of the building and thus would require such a permit to be taken out. These results are thus consistent with our results regarding condo conversion.

Treatment Effect Heterogeneity.—We now explore the heterogeneity in these effects between high and low house price appreciation zip codes. This analysis is motivated by our previous tenant regressions in which we found that landlords of rent-controlled buildings appear to have actively removed tenants in high appreci- ation zip codes. Here, we investigate whether landlords of rent-controlled apart- ments also disproportionately converted to condo or redeveloped buildings in high appreciation areas. Table 7 reports the average treatment effects within high and low appreciation zip codes. We find a 21
percent decline
in the renter population and a
12 percent increase in the owner population within the high appreciation zip codes, v
ersus a 11 percent renter decline and 6 percent owner increase in lo w appreciation
areas. Further, we find condo conversions increase by 10 percent in high appre- ciation
zip codes versus 5.8 percent in
low appreciation areas. The conversion to
owner
-occupied housing may be especially lucrative in these high appreciation zip Panel A. Population/average population 1990–1994 Panel B. Renters/average population 1990–1994 −0.4
−0.3
−0.2
−0.1
0 0

0.1 −0.4
−0.3
−0.2
−0.1

0.1 −0.4
−0.3
−0.2
−0.1
0 0.1
Population/average
population 1990–1994
Renters/average
population 1990–1994
1990 1995 2000 2005 2010
1990 1995 2000 2005 2010
1990 1995 2000 2005 2010
Panel C. Owners/average population 1990–1994
Owners/average
population 1990–1994
β = 0.081 (0.041)
β = −0.064 (0.093) β = −0.145 (0.075)
Figure 7. Treatment Effect for Multi-Family Residence ( 2–4 Units ) Notes: Sample consists of all multi-family residences with 2–4 units in San Francisco that were built during 1900–1990 . The treatment ef
fects along with 90 percent CI are plotted. Standard errors are clustered at the parcel lev
el. The average treatment effects in the post-2006 period and their standard errors are reported in the upper-left corner.
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codes as they likely have higher income residents. In contrast, we find a larger effect (9.3 percent versus 3.2 percent)
of properties being knocked down and rebuilt in low
appreciation areas than high priced areas. This effect is possibly driven by land use regulations making it very hard to build new construction in high-end areas of San Francisco.
20 Overall, these effects reaffirm that the landlords remove rental housing stock in those areas where it is most profitable to do so.

Gentrification Effects.—The previous section shows that rent control incentiv- ized landlords to substitute away from an older rental housing stock toward new construction rentals and owner-occupied condos. Combining our estimates of rent control’s effect on the number of owner occupants (8.1
percent
) and renters liv-
ing in rent control exempt housing (7.2 percent
) suggests that 15.3 percent of the
treated properties engaged in reno
vations to evade rent control. Since these types of
20 Most new construction in San Francisco has occurred in neighborhoods that historically were dominated by industry and warehouses.
−0.4
−0.3
−0.2
−0.1
0 0.1
Renters in rent-controlled buildings1990 1995 2000 2005 2010 −0.4
−0.3
−0.2
−0.1

0.1 0
Renters in redeveloped buildings1990 1995 2000 2005 2010 0 0.02

0.04 0.06

0.08 0.1
Conversion
Panel C. Conversion Panel D. Cumulative add/alter/repair per unit Panel A. Renters in rent-controlled
buildings/average population, 1990–1994
Panel B. Renters in redeveloped
buildings/average population, 1990–1994
1990 1995 2000 2005 2010 2015
−0.02

0.02 0

0.04 0.06

0.08 Cumulative add/alter/repair per unit
1990 1995 2000 2005 2010 2015
β = 0.079 (0.006)
β = −0.246 (0.077) β = 0.072 (0.023)
β = 0.046 (0.014)
Figure 8. Treatment Effect for Multi-Family Residence ( 2–4 Units ) Notes: Sample consists of all multi-family residences with 2−4 units in San Francisco that were built during 1900−1990 . The treatment ef
fects along with 90 percent CI are plotted. Standard errors are clustered at the parcel lev
el. The average treatment effects in the post-2006 period and their standard errors are reported in the upper-left corner.
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 renovations create housing that likely caters to high income tastes, rent control may have fueled the gentrification of San Francisco. To assess this, we compare the 2015 residents living in properties treated by rent control to those living in the control buildings in 2015. While we do not have data directly on the income levels of the 2015 residents of these properties, we can use the historical neighborhood choices of these tenants as a proxy for their income. Intuitively, if residents of treated build- ings used to live in high-end neighborhoods, while residents of control buildings used to live in low-end neighborhoods, we can infer that the residents of treated buildings are likely to be higher income. Specifically, we take all residents in the treatment and control buildings as of 2015. We then look at their addresses as of 2010, five years prior. We geocode these 2010 addresses to census block groups and measure the block group per capita income of their 2010 address, from the ACS.

We find that properties treated by rent control have tenants who came from neigh- borhoods with $1,292 higher per capita incomes (standard error of 522) ,
represent-
ing a 2.8 percent increase, relativ
e to residents of control group buildings located
in the same zip code. 21 This 2.8 percent increase represents the av erage income increase across all properties treated by rent control. Since only 15.3 percent of these properties upgraded their housing stock, we would e xpect these high income
residents to only be drawn into this 15.3 percent. Indeed, the other 85 percent of the treated housing stock that did not renov
ate may have lower income residents due to
the direct effect of rent control on tenant mobility. To construct a lower bound esti- mate of the effect of rent control on gentrification, we will assume that residents of 21 The full regression details are reported in online Appendix Table A3.

Table 7—Treatment Effect Heterogeneity for Multi-Family Parcels by House Price Appreciation
High appreciation Low appreciation
(1) (2)
Population/average population 1990–1994 −0.092 −0.050
(0.176) (0.108)
Renters/average population 1990–1994 −0.207 −0.112
(0.144) (0.085)
Renters in rent-controlled −0.284 −0.225
b
uildings/average population 1990–1994 (0.148) (0.088)
Renters in redeveloped 0.032 0.093
b
uildings/average population 1990–1994 (0.058) (0.016)
Owners/average population 1990–1994 0.116 0.063
(0.066) (0.052)
Conversion 0.100 0.058
(0.011) (0.006)
Cumulative 0.016 0.061
Add
/alter/repair per unit (0.03) (0.015)
Notes: Sample consists of all multi-family residences with 2–4 units in San Francisco that were built during 1900–1990. We divide tenants into two groups by whether their 1993 zip code experienced above- or below-median house price appreciation during 1990–2000. Columns 1 and 2 report the average treatment effects for various parcel level outcomes in the post-2006 period for residences in the high and low appreciation areas, respectively. Standard errors in parentheses are clustered at the parcel level.
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the non-renovated housing stock have incomes similar to that of the control group.

Under this assumption, our estimate of a 2.8 percent increase in residents’ incomes suggests that the renov
ated buildings attracted residents with at least 18 percent (

2.8/0.153) higher incomes than residents of control group buildings in the same zip code. In this way, rent control appears to have brought higher income residents into San Francisco, fueling gentrification.

C. Impacts on Inequality
Taking our results all together, it appears rent control has substantively different impacts on income inequality in the short versus long run. In the short run, rent control prevents displacement of the initial 1994 tenants from San Francisco, espe- cially among racial minorities. To the extent that these 1994 tenants are of lower income than those moving into San Francisco over the following years, rent control increases income inequality. However, this short-term effect decays over time. Eight years after the law change, 4.5 percent of the tenants treated by rent control were able to remain in San Francisco because of rent control. However, five years later, this effect had decayed to 3.7 percent, and will likely continue to decline in the future.

In the long run, on the other hand, landlords are able to respond to the rent con- trol policy change by substituting toward types of housing exempt from rent control price caps, upgrading the housing stock, and lowering the supply of rent-controlled housing. Indeed, the prior section showed that as of 2015, the average property treated by rent control has higher income residents than similar market rate prop- erties. The long-term landlord response thus offsets rent control’s initial effect of keeping lower income tenants in the city by replacing them with residents of above-average income. In this way, rent control works to increase income inequality in both the short run and in the long run, but through different means. Rent control’s short-term effects increases the left tail of the income distribution, while the long- term effects increase the right tail.

In addition to widening income inequality, rent control has unequal effects on tenants living in San Francisco at the time of the law change and future tenants of the city. Incumbent tenants already living in San Francisco who get access to rent control as part of the law change are clearly made better off as indicated by their preference to remain in their rent-controlled apartment. However, this comes at the expense of future renters in San Francisco, who must bear higher rents due to the endogenous reductions in rental supply. In this way, the law served as a transfer from future renters in the city to renters in 1994, creating economic well-being inequal- ity between incumbent and future renters of San Francisco. Our companion paper (Diamond, McQuade, and Qian 2018) performs a fully quantitative analysis of these welfare gains and losses through the lens of a dynamic discrete choice model of tenant migration and performs general equilibrium counterfactual analyses.

Since incumbent renters are made better off, it is not surprising that popular votes to expand rent control often pass in cities with high renter populations. The benefi- ciaries are the ones who are able to vote, while future renters who pay the costs of rent control do not get a say in these elections. Local popular votes thus appear to be an inefficient way to set rent control policies.
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DIAMOND ET AL.: THE EFFECTS OF RENT CONTROL EXPANSIONVOL. 109 NO. 9 IV . Conclusion
In this paper, we have studied the impact of rent control on its tenant beneficiaries as well as the landlord response. To answer this question, we exploit a unique rent control expansion in San Francisco in 1994 that suddenly provided rent control pro- tections for small multi-family housing built prior to 1980. By combining new panel microdata on individual migration decisions with detailed assessor data on individ- ual parcels in San Francisco, we get quasi-experimental variation in the assignment of rent control at both the individual tenant level and at the parcel level.

We find that, on average, in the medium to long term the beneficiaries of rent control are between 10 and 20
percent more likely to remain at their 1994 address
relati
ve to the control group and, moreover, are more likely to remain in San Francisco. Further, we find the effects of rent control on tenants are stronger for racial minorities, suggesting rent control helped prevent minority displacement from San Francisco. All our estimated effects are significantly stronger among older households and among households that have already spent a number of years at their current address. On the other hand, individuals in areas with quickly rising house prices and with few years at their 1994 address are less likely to remain at their cur- rent address, consistent with the idea that landlords try to remove tenants when the reward is high, through either eviction or negotiated payments.

We find that landlords actively respond to the imposition of rent control by con- verting their properties to condos and TICs or by redeveloping the building in such as a way as to exempt it from the regulations. In sum, we find that impacted land- lords reduced the supply of available rental housing by 15 percent. Further, we find
that there w
as a 25 percent decline in the number of renters living in units protected by rent control, as man
y buildings were converted to new construction or condos that are exempt from rent control.

This reduction in rental supply likely increased rents in the long run, leading to a transfer between future San Francisco renters and renters living in San Francisco in 1994. In addition, the conversion of existing rental properties to higher-end, own- er-occupied condominium housing ultimately led to a housing stock increasingly directed toward higher income individuals. In this way, rent control contributed to the gentrification of San Francisco, contrary to the stated policy goal. Rent control appears to have increased income inequality in the city by both limiting displace- ment of minorities and attracting higher income residents.

These results highlight that forcing landlords to provide insurance against rent increases can ultimately be counterproductive. If society desires to provide social insurance against rent increases, it may be less distortionary to offer this subsidy in the form of government subsidies or tax credits. This would remove landlords’ incentives to decrease the housing supply and could provide households with the insurance they desire. A point of future research would be to design an optimal social insurance program to insure renters against large rent increases. 3394
THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2019

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Monterey County Association of Realtors®
5 Harris Court, Building A
Monterey CA, 93940
October 24, 2023
Mayor Kimbley Craig
Councilmember Tony Barrera
Councilmember Carla Viviana González
Councilmember Steve McShane
Councilmember Orlando Osornio
Councilmember Anthony Rocha
Councilmember Andrew Sandoval
Re. Agenda item 23-648: Rent Stabilization and Tenant Protection Honorable Mayor and City Councilmembers,
Now is the time to seek collaborative, innovative, and pro-housing solutions to our housing availability and affordability crisis. This is especially true for rental housing; renters are in need to help and we agree steps must be taken to do so. However, policies that divide our community, punish housing providers, and disincentivize growth in housing will achieve the opposite.