Local Adaptation to Climate Risks in the Rental Housing Market

Last registered on July 13, 2026

Pre-Trial

Trial Information

General Information

Title
Local Adaptation to Climate Risks in the Rental Housing Market
RCT ID
AEARCTR-0019106
Initial registration date
July 09, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
July 13, 2026, 8:00 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
HEC Lausanne

Other Primary Investigator(s)

PI Affiliation
EDHEC Business School

Additional Trial Information

Status
In development
Start date
2026-07-09
End date
2028-09-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Where households choose to live is central to how they adapt to climate risks, yet little is known about adaptation in the rental market, where policy must work through regulation, prices, and information. We study heat-stress adaptation among renters in Paris—a city with pronounced urban-heat-island exposure and neighborhood-based rent control—through a framed field experiment. Renters who intend to move complete a two-part study: a neighborhood-choice module and a real apartment search on a live listing portal, tracked via a browser extension. Within subjects, we deliver information treatments about heat exposure (projected tropical nights) and rent-control ceilings at two levels of aggregation, the neighborhood and the apartment, and elicit incentivized moving intentions before and after each treatment. Between subjects, we randomize incentivized versus hypothetical elicitation, the on-listing salience of the information, and the size of a moving-cost voucher. Using this experimental variation, we estimate a dynamic neighborhood–apartment choice model and recover the willingness to pay to avoid heat at the neighborhood versus apartment level, the disutility of rent, and moving costs. The estimates let us simulate counterfactual information, rent-control, and place- versus building-based adaptation policies, and their welfare consequences.
External Link(s)

Registration Citation

Citation
Fanghella, Valeria and Sebastien Houde. 2026. "Local Adaptation to Climate Risks in the Rental Housing Market." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.19106-1.0
Experimental Details

Interventions

Intervention(s)
Participants are renters who intend to move in the Paris region. In an online study, they report where they would consider moving and how likely they are to move to each option, and they complete a real apartment search on a live listing portal. The intervention consists of information provided during the study: participants are shown objective, science-based information about two neighborhood and apartment attributes—exposure to heat (projected number of "tropical nights") and the applicable rent-control ceiling. This information is delivered at two levels of aggregation, the neighborhood and the individual apartment, and participants report their moving intentions before and after receiving it. Additional randomized elements govern whether the intention questions carry a monetary incentive for accuracy, whether the heat and rent information is displayed directly on listings during the apartment search, and the size of a hypothetical moving-cost voucher.
Intervention Start Date
2026-07-09
Intervention End Date
2026-10-31

Primary Outcomes

Primary Outcomes (end points)
Intention to move, which we elicit several times during the survey. We use those to conduct a reduced-form and structural estimation.
Primary Outcomes (explanation)
We relate the intention to move to a structural dynamic model of housing choice. This enables us to do a structural estimation of some of the parameters of the model. The parameters that we focus on are the behavioral responses to heat stress and rent control at the neighborhood and apartment level.

Secondary Outcomes

Secondary Outcomes (end points)
Our design also allows us to estimate other structural parameters. Ultimately, our goal is to have a fully specified model, for which we estimate the moving costs and preference heterogeneity for apartment attributes.

Our deisgn also allows to estimate heterogeneous parameters, which we will investigate across demographics.
Secondary Outcomes (explanation)
We map the intention to move to a structural model and use our elicitation procedure to do a full structural estimation of the model. The main parameters (primary outcomes) are point-identified. Other parameters are identified with more structure. All the data used for the estimation comes from our procedure.

Experimental Design

Experimental Design
The study uses a within-subject design in two parts. In the first part, participants consider neighborhoods in Paris and report how likely they are to move to each; in the second part, they conduct a real apartment search online and report their intentions over specific apartments. In both parts, participants report their intentions before and after receiving information about local attributes, so that each participant serves as their own comparison. Across participants, we additionally vary a small number of features of the task—including whether the intention questions carry an accuracy-based reward, how the information is presented during the apartment search, and the size of a moving-cost scenario. Moving intentions are the primary outcomes.
Experimental Design Details
Not available
Randomization Method
The randomization of the information treatment (salience or not at the begining of Part 2) will be done via Qualtrics. The winners of the rewards will be selected after the completion by all participants. The winners will be selected with a randomization done in office by a computer. The question that will be incentivized will also be done with a randomization done in office by a computer.
Randomization Unit
Participant-level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
NA
Sample size: planned number of observations
1500 potential renters
Sample size (or number of clusters) by treatment arms
We will randomize half the participants in the treatment arm with salience information at the beginning of Part 2 of the study.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We will report individual-level estimates and the overall distribution of our parameters. We have maximized the sample size given our budget and use a 50/50 randomization to increase the chance of finding an effect on salience treatment. The salience treatment is, however, a main outcome and hypothesis. The full estimation of our structural model only requires 100 observations or even less, given that we have a within-subject design and few parameters identified with cross-subject variation.
IRB

Institutional Review Boards (IRBs)

IRB Name
Commission/Comité d’Éthique de la Recherche (Research Ethics Committee) HEC
IRB Approval Date
2026-07-09
IRB Approval Number
N/A