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Field
Abstract
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Before
We designed a field experiment to measure how housing price expectations affect real estate transactions in the United States. Our motivation stems from the fact that home price expectations play a prominent role in many accounts of the housing boom that occurred during the mid-2000s. We will launch a large-scale high-stakes information experiment with tens of thousands of households who have recently listed their houses for sale.
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After
We designed a field experiment to measure how housing price expectations affect real estate transactions in the United States. Our motivation stems from the fact that home price expectations play a prominent role in many accounts of the housing boom that occurred during the mid-2000s. We will launch a large-scale high-stakes information experiment with 60,000 households who have recently listed their houses for sale.
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Field
Trial Start Date
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Before
June 10, 2019
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After
June 04, 2019
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Field
Last Published
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Before
July 19, 2019 11:58 AM
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After
September 17, 2019 04:05 AM
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Field
Intervention (Public)
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Before
We will send letters to tens of thousands of individuals who have listed their houses on the market.
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After
We will send letters to 60,000 individuals who have listed their houses on the market.
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Field
Intervention Start Date
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Before
June 10, 2019
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After
June 04, 2019
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Field
Intervention End Date
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Before
July 31, 2019
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After
July 04, 2019
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Field
Primary Outcomes (End Points)
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Before
The primary outcomes correspond to the market behavior of the subjects: the transaction dates and prices.
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After
The primary outcomes correspond to the market behavior of the subjects.
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Field
Primary Outcomes (Explanation)
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Before
The main outcomes are: i. the time elapsed from the day of letter delivery until the time the property was sold; ii. the price at which the house was sold.
The variable "time until sale" will be right-censored at the time we collect the data (e.g., if a house has not been sold 3 months after the intervention, we do not know if it will be sold 3, 4, 6... months after the intervention). We can use standard methods to account for the censoring. A second (minor) caveat is that some houses may be actively taken off the market. We do not expect this behavior to be common, but if it is then we may need to include it as a separate outcome.
The variable "price sold" will also be censored for some subjects, because the price is observed only if the home is sold. We will use standard methods to deal with this censoring. Additionally, we will have an "intermediate" variable related to the sales price: the listing price. We will use publicly available data to track changes in the list price, and use it as a separate outcome. The advantages of this alternative outcome are: i. It is not subject to censoring; ii. The list price may reflect the price that the seller "hopes" to sell the house for and thus it may be more elastic to housing expectations. However, ex-ante, we do not know if changes in listing price are frequent enough to give us enough statistical power when considered as the outcome variable.
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After
The market outcomes are: i. the time elapsed from the day of letter delivery until the time the property was sold; ii. the price at which the house was sold.
The variable "time until sale" will be right-censored at the time we collect the data (e.g., if a house has not been sold 3 months after the intervention, we do not know if it will be sold 3, 4, 6... months after the intervention). We can use standard methods to account for the censoring. The variable "price sold" will also be censored for some subjects, because the price is observed only if the home is sold. We will use standard methods to deal with this censoring.
Additionally, there are two "intermediate" forms of behavior related to the main outcomes that we may be able to study: i. Whether the property was actively taken off the market; ii. the listing price.
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Field
Experimental Design (Public)
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Before
This is an information provision experiment. Subjects will be randomly assigned to different treatment arms, which result in different information being included in the letter.
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After
This is an information provision experiment. Subjects will be randomly assigned to different treatments, which result in different signals about the future house prices being included in the letter.
In a deceptive design, we would just randomize the signal given to the subjects: e.g., we tell them with 50% probability that the housing prices will increase by 1%, and we tell them with 50% probability that the housing prices will increase by 10%. Instead, we use a non-deceptive design: we randomize the subject into one of multiple valid signals about the home price dynamics (e.g., forecasts produced by different econometric models).
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Field
Planned Number of Observations
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Before
We will send 60,000 letters. It is important to note that the letters will take over a week to be delivered. As a result, by the time the letter was delivered, we anticipate that a significant fraction of these 60,000 individuals will have sold their properties already. We will only analyze the effects on the subjects who did not sell their houses by our estimated delivery date (because it is impossible for our letter to affect a transaction that has happened already). We can use the rest of the sample for a falsification test in an event-study fashion.
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After
We will send 60,000 letters. It is important to note that there is a significant delay (i.e., weeks) from the time that the data is acquired to the actual delivery of the letters. As a result, by the time the letter was delivered, we anticipate that a non-trivial fraction of these 60,000 individuals will have sold their properties already. We will only analyze the effects on the subjects who did not sell their houses by our estimated delivery date (because it is impossible for our letter to affect a transaction that has happened already). If there is enough power, we can use the rest of the sample for a falsification test in an event-study fashion.
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Field
Sample size (or number of clusters) by treatment arms
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Before
Subjects will be randomized into the following letter types: 20% to the "present" type; 30% to the "past" type and 50% to the "future" type.
Within the "past" letter type, subjects will be randomized with equal probability to 1-year or 2-year sub-treatments. Within the "future" letter type, subjects will be randomized with equal probability to one of the three possible forecast sub-treatments.
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After
Subjects will be randomized into the following letter types: 20% to the "present" type; 30% to the "past" type and 50% to the "future" type. Within the "past" letter type, subjects will be randomized with equal probability to 1-year or 2-year sub-treatments. Within the "future" letter type, subjects will be randomized with equal probability to one of the three possible forecast sub-treatments.
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Field
Intervention (Hidden)
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Before
We will send letters to a sample of tens of thousands of individuals who have listed their houses on the market. Subjects will be randomly assigned to different types of letters, each containing different types of information about the evolution of home prices. We will use publicly available information to measure the effects of the information contained in the letters on the date and price of the transaction.
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After
We will send letters to a sample of 60,000 individuals who have listed their houses on the market. Subjects will be randomly assigned to different types of letters, each containing different signals about the evolution of home prices. We will use publicly available information to measure the effects of the information contained in the letters on individual market behavior (e.g., whether the property was sold).
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Field
Secondary Outcomes (End Points)
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Before
Survey measures of the housing price expectations of the subjects.
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Survey measures on housing price expectations.
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Field
Secondary Outcomes (Explanation)
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Before
We will collect survey data in two ways. First, the letter sent to the subjects will also include a URL to complete an online survey. A sample of this online survey is attached to this registration. This survey includes questions about the expected future median price in the ZIP code of the respondent (1 year ahead and 5 years ahead).
There is a high likelihood that this survey data will not be useful. First, based on similar surveys, the response rate to the online survey is probably going to be very low. Also, it is possible that treatments affect the response rate, thus challenging the internal validity of the analysis of the survey data. For these reasons, we will conduct a complementary survey experiment with an auxiliary sample (Amazon Mechanical Turk workers). In that auxiliary survey, we will not be able to measure the primary outcomes (i.e., market transactions), but we will be able to measure the secondary outcomes (i.e., survey expectations). We will deploy that auxiliary survey at around the same date in which we will send the letters. And we will also attempt to conduct a follow-up survey with this auxiliary sample a month after the baseline survey. Please find attached screenshots of the baseline and follow-up auxiliary surveys.
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After
What we really care is whether changes in housing price expectations affect market behavior. The effects of the experimental information on behavior is the "reduced form" regression. We will collect survey data on housing price expectations to estimate the "first stage" regression: i.e., the effect of the experimental information on housing price expectations.
We will collect survey data in two ways. First, the letter sent to the subjects will also include a URL to complete an online survey. A sample of this online survey is attached to this registration. This survey includes questions about the expected future median price in the ZIP code of the respondent (1 year ahead and 5 years ahead).
There is a high likelihood that this survey data will not be useful. First, based on similar surveys, the response rate to the online survey is probably going to be very low. Also, it is possible that treatments affect the response rate, thus challenging the internal validity of the analysis of the survey data. Relatedly, it is possible that the sample of subjects responding to this survey is highly unrepresentative. For these reasons, we will conduct a complementary survey experiment with an auxiliary sample (Amazon Mechanical Turk workers). In that auxiliary survey, we will not be able to measure the primary outcomes (i.e., market transactions), but we will be able to measure the secondary outcomes (i.e., survey expectations). We will deploy that auxiliary survey at around the same date in which we will send the letters. And we will also attempt to conduct a follow-up survey with this auxiliary sample a month after the baseline survey. Please find attached screenshots of the baseline and follow-up auxiliary surveys.
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