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| Main Authors: | , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19568367 |
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Table of Contents:
- <p class="MsoNormal"><strong><span>The property technology platforms have experienced increasing demand of intelligent system extending<span> </span>past<span> </span>elementary<span> </span>listing<span> </span>and<span> </span>booking<span> </span>features. This paper introduces Lyvo+, a smart Prop Tech platform that aims to solve the major loopholes in co- living management with the help of a dual-engine design. The initial engine is an assessment of social compatibility between potential roommates based on these lifestyle preferences and personal habits with a Generative<span> </span>AI point that generates a natural language description of the match recommendation. The second engine<span> </span>is<span> </span>concerned<span> </span>with<span> </span>operational<span> </span>efficiency<span> </span>with<span> </span>the help of a rent prediction model based on machine learning and a Natural Language Processing (NLP) system, which sorts maintenance requests in terms of urgency.<span> </span>The<span> </span>results<span> </span>show<span> </span>that<span> </span>social<span> </span>intelligence<span> </span>along with the predictive analytics will result in more transparent and efficient co-living experiences. Social Compatibility Matching, Rent Prediction and Maintenance Prioritization are three main intelligent components<span> </span>that<span> </span>are<span> </span>used<span> </span>by<span> </span>Lyvo+<span> </span>to<span> </span>provide<span> </span>the<span> </span>data- driven decision-support technology to co-living <span>environments.</span><span> </span></span></strong></p>