--- 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](https://arxiv.org/abs/2502.16810). ## 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. 1. **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. 2. **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. 3. **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. 4. **"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. 5. **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. 6. **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. 7. **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. 8. **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.