Diamond et al. (2019)

The economics of Housing & Homelessness

A Class Presentation of

The paper's motivation

What do you think about the way the paper motivates itself?

Certain states are considering repealing laws which limit a cities ability to pass/expand rent control

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There is a lot of economic research that highlights the negative consequences of rent control

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Maybe the fact that tenants cannot insure themselves against future rent increases is one reason we should consider rent control?

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Rent Control Model

What Additional Insight Can We Gain

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

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Understanding the Magnitude

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Understanding how the Long-term Effects Differ from Short-term

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Rent Control Increase Gentrification

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

Y_{izst} = \delta_{zt} + \alpha_i + \beta_tT_i + \gamma_{st} + \varepsilon_{it}

Y_{izst} = \delta_{zt} + \alpha_i + \beta_tT_i + \gamma_{st} + \varepsilon_{it}

Regression Model

Individual Level Fixed Effects

Indicator for Moved

Rent Control Indicator

Date of Move in by Time Fixed Effects

Tenancy by Year Fixed Effects

Applied Econometrics

Nonparametric Identification

You should in practice be able to run a separate model for each treatment effect

Y_{i} \perp D_i \vert X_i

Y_{i} \perp D_i \vert X_i

Conditional on Zip Code, Year of Move-in, and Length of Tenancy, Rent Control is as good as randomly assigned

\beta_1 = \mathbb{E}_x\big[\mathbb{E}[Y_i \vert X_i = x_j, D=1] - \mathbb{E}[Y_i \vert X_i = x_j, D=0]\big]

\beta_1 = \mathbb{E}_x\big[\mathbb{E}[Y_i \vert X_i = x_j, D=1] - \mathbb{E}[Y_i \vert X_i = x_j, D=0]\big]

Pause for Concern?

Zip Code is quite large, right?

How different are these buildings in the treated and control groups? What are the monthly rents? What is the income of the tenants?

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This is not difference-in-difference

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"As further evidence of random assignment, we see no pre-trends leading up to time of treatment."

Pre-trends aren't meaningful in this context because the outcomes are of a different nature

(Magnitude of effects drop with census tract level fixed effects)

It's surprising that a paper on rent control doesn't make use of rent data and therefore doesn't show that the rent control measure is binding

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Zip Codes seem too broad a control in this setting. Census tracts are available. I'm not sure why the authors don't include census tract fixed effects in their main specification (they show in one regression that census fixed effects make the treatment effect smaller)

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The time-frame seems to broad

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Treated

1900

1900

1979

1979

Control

1980

1980

1990

1990

In the medium to long term the beneficiaries of rent control are between 10 and 20% more likely to remain at their 1994 address

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1

Effects of rent control on tenants are stronger for

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Minorities

Older Households

Longer tenancies

Effects of rent control on tenants are negative for

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Areas with rising housing prices

Results

What catches your attention?

Impacted landlords reduced the supply of available rental housing by 15%

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4

What's missing from these results?

Diamond et al. (2019)

The economics of Housing & Homelessness

The paper's motivation

Rent Control Model

Applied Econometrics

San Francisco

San Francisco

Identification

Check for Balance

Overview

Motivation

clarifying questions

Notes for Researchers