dataset_info:
features:
- name: bedrooms
dtype: float64
- name: bathrooms
dtype: float64
- name: price
dtype: float64
- name: description
dtype: string
- name: living_area_value
dtype: float64
- name: lot_area_value
dtype: float64
- name: area_units
dtype: string
- name: brokerage_name
dtype: string
- name: home_type
dtype: string
- name: time_on_zillow
dtype: string
- name: page_view_count
dtype: float64
- name: favorite_count
dtype: float64
- name: home_insights
list: string
- name: neighborhood_region
dtype: string
- name: city
dtype: string
- name: state
dtype: string
- name: year_built
dtype: float64
- name: county
dtype: string
- name: monthly_hoa_fee
dtype: float64
- name: is_for_auction
dtype: bool
- name: is_new_home
dtype: bool
- name: is_FSBO
dtype: bool
- name: is_FSBA
dtype: bool
- name: is_foreclosure
dtype: bool
- name: is_bank_owned
dtype: bool
- name: is_coming_soon
dtype: bool
- name: is_pending
dtype: bool
- name: is_open_house
dtype: bool
- name: associations
list:
- name: feeFrequency
dtype: string
- name: name
dtype: string
- name: phone
dtype: string
- name: annual_hoa_fee
dtype: string
- name: has_basement
dtype: bool
- name: appliances
list: string
- name: cooling
list: string
- name: can_raise_horses
dtype: bool
- name: covered_parking_capacity
dtype: float64
- name: fees_and_dues
list:
- name: type
dtype: string
- name: fee
dtype: string
- name: name
dtype: string
- name: phone
dtype: string
- name: fencing
dtype: string
- name: fireplace_features
list: string
- name: fireplaces
dtype: float64
- name: flooring
list: string
- name: is_furnished
dtype: bool
- name: garage_parking_capacity
dtype: float64
- name: has_association
dtype: bool
- name: has_attached_garage
dtype: bool
- name: has_attached_property
dtype: bool
- name: has_cooling
dtype: bool
- name: has_carport
dtype: bool
- name: has_fireplace
dtype: bool
- name: has_garage
dtype: bool
- name: has_heating
dtype: bool
- name: has_land_lease
dtype: bool
- name: has_spa
dtype: bool
- name: has_view
dtype: bool
- name: heating
list: string
- name: high_school
dtype: string
- name: interior_features
list: string
- name: laundry_features
list: string
- name: levels
dtype: string
- name: lot_features
list: string
- name: middle_or_junior_school
dtype: string
- name: parking_capacity
dtype: float64
- name: parking_features
list: string
- name: patio_and_porch_features
list: string
- name: pool_features
list: string
- name: price_per_square_foot
dtype: float64
- name: roof_type
dtype: string
- name: security_features
list: string
- name: sewer
list: string
- name: stories
dtype: float64
- name: subdivision_name
dtype: string
- name: utilities
list: string
- name: view
list: string
- name: water_source
list: string
- name: window_features
list: string
- name: architectural_style
dtype: string
- name: construction_materials
list: string
- name: exterior_features
list: string
- name: foundation_details
list: string
- name: has_additional_parcels
dtype: bool
- name: has_home_warranty
dtype: bool
- name: is_new_construction
dtype: bool
- name: listing_terms
dtype: string
- name: elementary_school
dtype: string
- name: bathrooms_full
dtype: float64
- name: bathrooms_half
dtype: float64
- name: avg_school_rating
dtype: float64
- name: id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 3794978
num_examples: 1883
download_size: 1619909
dataset_size: 3794978
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-sa-4.0
language:
- en
tags:
- economy
- real_estate
Processed Listing Data for the Paper "AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting"
Author Team: {Jibang Wu*, Chenghao Yang*}, Yi Wu, Simon Mahns, Chaoqi Wang, Hao Zhu, Fei Fang, Haifeng Xu.
"*" indicates an equal contribution. Read the Paper.
Reference
If you use this data as part of any published research, please acknowledge the following paper:
@article{wu2025grounded,
title={AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting},
author={Wu, Jibang and Yang, Chenghao and Wu, Yi and Mahns, Simon and Wang, Chaoqi and Zhu, Hao and Fang, Fei and Xu, Haifeng},
journal={arXiv preprint arXiv:2502.16810},
year={2025}
}
Description
This repository contains a dataset of real estate listings, intended strictly for non-commercial, research, and educational purposes. The data was collected from publicly available listings via a third-party, billed API. The primary goal of this project is to provide researchers, students, and data scientists with a high-quality dataset for exploring trends in the real estate market, building predictive models, and conducting academic studies.
Key Features:
- Anonymized Data: All data has been processed to remove Personally Identifiable Information (PII) to protect the privacy of individuals.
- Structured Format: The data is provided in a clean, easy-to-use format.
- Rich Attributes: Includes various property attributes such as price, size, number of bedrooms/bathrooms, and more.
License and Terms of Use
This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.
In addition to the CC BY-NC-SA 4.0 license, you must adhere to the following Disclaimer and Terms of Use.
Dataset Disclaimer and Terms of Use
IMPORTANT: READ CAREFULLY. By downloading, accessing, or using the dataset provided (the "Dataset"), you ("User") agree to be legally bound by the terms and conditions set forth in this Disclaimer and Terms of Use ("Agreement"). If you do not agree to these terms, do not download, access, or use the Dataset.
Purpose and Grant of License
The Dataset is provided for non-commercial, research, and educational purposes only. The provider of this Dataset grants the User a limited, non-exclusive, non-transferable, revocable license to use, copy, and analyze the Dataset strictly for such purposes.
Prohibited Uses
Use of the Dataset for any commercial purpose is strictly prohibited. For the avoidance of doubt, "commercial purpose" includes, but is not limited to:
- Resale, sublicensing, or distribution of the Dataset, in whole or in part, for a fee.
- Integration or use of the Dataset in any commercial product, service, or application.
- Use of the Dataset for commercial consulting, business intelligence, lead generation, or marketing.
- Any use that directly or indirectly generates revenue or is intended for monetary gain.
Data Source and Third-Party Rights
The data contained herein was collected from publicly available real estate listings, accessed via a third-party, billed Application Programming Interface (API).
No Endorsement: The provider of this Dataset is not affiliated with, endorsed by, or sponsored by the original data source (e.g., Zillow Group, Inc. or any other real estate platform). All trademarks, service marks, and logos are the property of their respective owners.
User Responsibility: While the original data is publicly accessible, the compilation, organization, and terms of service of the third-party platform (the "Data Source") may impose its own restrictions on data use. It is the sole responsibility of the User to review and comply with the terms of service of the original Data Source. The provider of this Dataset makes no representations or warranties regarding the legality of the User's use of this data and disclaims any liability for the User's failure to comply with third-party terms.
"AS IS" Disclaimer of Warranty
THE DATASET IS PROVIDED "AS IS" AND "AS AVAILABLE," WITHOUT ANY WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED. THE PROVIDER OF THE DATASET EXPLICITLY DISCLAIMS ALL WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, ACCURACY, COMPLETENESS, TIMELINESS, AND NON-INFRINGEMENT. THE PROVIDER DOES NOT WARRANT THAT THE DATASET WILL BE ERROR-FREE OR THAT ANY DEFECTS WILL BE CORRECTED.
Privacy and Personally Identifiable Information (PII)
Reasonable efforts have been made to process the data and remove or anonymize Personally Identifiable Information (PII). However, the complete absence of PII cannot be guaranteed. The User agrees to handle the Dataset with care and is solely responsible for:
Ensuring their use of the Dataset complies with all applicable privacy laws and regulations (e.g., GDPR, CCPA).
Any consequences arising from the use of any PII that may remain within the Dataset.
Not attempting to re-identify any individuals from the anonymized data.
Limitation of Liability
IN NO EVENT SHALL THE PROVIDER OF THE DATASET BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS DATASET, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. THE USER ASSUMES ALL RISK AND RESPONSIBILITY FOR THEIR USE OF THE DATASET.
Indemnification
The User agrees to indemnify, defend, and hold harmless the provider of the Dataset from and against any and all claims, liabilities, damages, losses, or expenses, including reasonable attorneys' fees and costs, arising out of or in any way connected with the User's access to or use of the Dataset, including any violation of this Agreement.
Acceptance of Terms By downloading, accessing, or using this Dataset, you signify your full acceptance of this Agreement. This Agreement constitutes the entire agreement between the User and the provider concerning the subject matter herein.